| Title: | Cheminformatics Toolkit for R |
|---|---|
| Description: | ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures. |
| Authors: | Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke |
| Maintainer: | Thomas Girke <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 3.57.0 |
| Built: | 2024-06-30 03:13:21 UTC |
| Source: | https://github.com/bioc/ChemmineR |
Add a new descriptor type to the database. Normally descriptor types are added as needed, but if you are doing a parrallel data load you must pre-load the descriptor type to prevent duplicate defintion errors.
addDescriptorType(conn, descriptorType)addDescriptorType(conn, descriptorType)
conn |
Any database connection object. |
descriptorType |
The name of the descriptor. |
No return value.
Kevin Horan
## Not run: conn = initDb(...) addDescriptor(conn,"fp") ## End(Not run)## Not run: conn = initDb(...) addDescriptor(conn,"fp") ## End(Not run)
Adds new features to a database without adding any data. Note that if you are loading new data anyway, it is much more efficient to use the loadSdf function and include the new features then. This function will have to read all compounds out of the database first.
addNewFeatures(conn, featureGenerator)addNewFeatures(conn, featureGenerator)
conn |
A database connection object, such as is returned by |
featureGenerator |
A function which returns a data frame containing the
new features. It may also contain features which are
already in the database, these will simply be
ignored. See the description of |
No value is returned.
Kevin Horan
#create and initialize a new SQLite database conn = initDb("test.db") data(sdfsample) #just load the data with no features or descriptors ids=loadSdf(conn,sdfsample) addNewFeatures(conn, function(sdfset) data.frame(MW = MW(sdfset), rings(sdfset,type="count",upper=6, arom=TRUE)) ) unlink("test.db")#create and initialize a new SQLite database conn = initDb("test.db") data(sdfsample) #just load the data with no features or descriptors ids=loadSdf(conn,sdfsample) addNewFeatures(conn, function(sdfset) data.frame(MW = MW(sdfset), rings(sdfset,type="count",upper=6, arom=TRUE)) ) unlink("test.db")
AP/APset
Returns atom pair component of objects of class AP or APset as list of vectors.
ap(x)ap(x)
x |
Object of class |
...
List |
with one to many of following components: |
numeric |
atom pairs |
Thomas Girke
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", J Chem Inf Comput Sci.
Functions: SDF2apcmp, apset2descdb, cmp.search, cmp.similarity
## Instance of SDFset class data(sdfsample) sdfset <- sdfsample[1:50] sdf <- sdfset[[1]] ## Compute atom pair library ap <- sdf2ap(sdf) (apset <- sdf2ap(sdfset)) view(apset[1:4]) ## Return main components of APset object cid(apset[1:4]) # compound IDs ap(apset[1:4]) # atom pair descriptors ## Return atom pairs in human readable format db.explain(apset[1])## Instance of SDFset class data(sdfsample) sdfset <- sdfsample[1:50] sdf <- sdfset[[1]] ## Compute atom pair library ap <- sdf2ap(sdf) (apset <- sdf2ap(sdfset)) view(apset[1:4]) ## Return main components of APset object cid(apset[1:4]) # compound IDs ap(apset[1:4]) # atom pair descriptors ## Return atom pairs in human readable format db.explain(apset[1])
Container for storing the atom pair descriptors of a single compound as numeric vector. The atom pairs are used as structural similarity measures and for compound similarity searching.
Objects can be created by calls of the form new("AP", ...).
AP:Object of class "numeric"
signature(x = "AP"): returns atom pairs as numeric vector
signature(from = "APset", to = "AP"): as(apset, "AP")
signature(object = "AP"): prints summary of AP
Thomas Girke
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", J Chem Inf Comput Sci.
Related classes: SDF, SDFset, SDFstr, APset.
Functions: SDF2apcmp, apset2descdb, cmp.search, cmp.similarity
showClass("AP") ## Instance of SDFset class data(sdfsample) sdfset <- sdfsample[1:50] sdf <- sdfsample[[1]] ## Compute atom pair library ap <- sdf2ap(sdf) (apset <- sdf2ap(sdfset)) view(apset[1:4]) ## Return main components of APset object cid(apset[1:4]) # compound IDs ap(apset[1:4]) # atom pair descriptors ## Return atom pairs in human readable format db.explain(apset[1]) ## Coerce APset to other objects apset2descdb(apset) # returns old list-style AP database tmp <- as(apset, "list") # returns list as(tmp, "APset") # converst list back to APset ## Compound similarity searching with APset cmp.search(apset, apset[1], type=3, cutoff=0.2) plot(sdfset[names(cmp.search(apset, apset[6], type=2, cutoff=0.4))]) ## Identify compounds with identical AP sets cmp.duplicated(apset, type=2) ## Structure similarity clustering cmp.cluster(db=apset, cutoff = c(0.65, 0.5))[1:20,]showClass("AP") ## Instance of SDFset class data(sdfsample) sdfset <- sdfsample[1:50] sdf <- sdfsample[[1]] ## Compute atom pair library ap <- sdf2ap(sdf) (apset <- sdf2ap(sdfset)) view(apset[1:4]) ## Return main components of APset object cid(apset[1:4]) # compound IDs ap(apset[1:4]) # atom pair descriptors ## Return atom pairs in human readable format db.explain(apset[1]) ## Coerce APset to other objects apset2descdb(apset) # returns old list-style AP database tmp <- as(apset, "list") # returns list as(tmp, "APset") # converst list back to APset ## Compound similarity searching with APset cmp.search(apset, apset[1], type=3, cutoff=0.2) plot(sdfset[names(cmp.search(apset, apset[6], type=2, cutoff=0.4))]) ## Identify compounds with identical AP sets cmp.duplicated(apset, type=2) ## Structure similarity clustering cmp.cluster(db=apset, cutoff = c(0.65, 0.5))[1:20,]
Ranked set of 4096 most frequent atom pairs observed in the compound collection from DrugBank with a MW < 1000. Their atom pairs were generated with the sdf2ap function. The provided data frame is sorted row-wise by atom pair frequency and only the 4096 most frequent atom pairs are included. This data set can be used as predefined atom pair selection when computing atom pair fingerprints with the desc2fp function.
data(apfp)data(apfp)
Object of class data.frame. First column contains atom pair (AP) IDs and the second column their frequency in DrugBank compounds.
Object stores 4096 most frequent atom pairs generated from DrugBank compounds.
DrugBank: http://www.drugbank.ca/
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", J Chem Inf Comput Sci.
data(apfp) apfp[1:4,]data(apfp) apfp[1:4,]
APset object
Atom pairs for 100 molecules stored in sdfsample.
data(apset)data(apset)
Object of class apset
Object stores atom pairs of 100 molecules.
apset <- sdf2ap(sdfsample)
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", J Chem Inf Comput Sci.
data(apset) apset[1:4] view(apset[1:4])data(apset) apset[1:4] view(apset[1:4])
List-like container for storing the atom pair descriptors of a many compounds as objects of class AP. This container is used for structure similarity searching of compounds.
Objects can be created by calls of the form new("APset", ...).
AP:Object of class "list"
ID:Object of class "character"
signature(x = "APset"): subsetting of class with bracket operator
signature(x = "APset"): returns single component as AP object
signature(x = "APset"): replacement method for single AP component
signature(x = "APset"): replacement method for several AP components
signature(x = "APset"): returns atom pair list from AP slot
signature(x = "APset"): concatenates two APset containers
signature(x = "APset"): returns all compound identifiers from ID slot
signature(x = "APset"): replacement method for compound identifiers in ID slot
signature(from = "APset", to = "AP"): as(apset, "AP")
signature(from = "APset", to = "list"): as(apset, "list")
signature(from = "list", to = "APset"): as(list, "APset")
signature(x = "APset"): returns number of entries stored in object
signature(object = "APset"): prints summary of APset
signature(x = "APset"): prints extended summary of APset
Thomas Girke
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", in J Chem Inf Comput Sci.
Related classes: SDF, SDFset, SDFstr, AP, FPset, FP.
Functions: SDF2apcmp, apset2descdb, cmp.search, cmp.similarity
showClass("APset") ## Instance of SDFset class data(sdfsample) sdfset <- sdfsample[1:50] sdf <- sdfsample[[1]] ## Compute atom pair library ap <- sdf2ap(sdf) (apset <- sdf2ap(sdfset)) view(apset[1:4]) ## Return main components of APset object cid(apset[1:4]) # compound IDs ap(apset[1:4]) # atom pair descriptors ## Return atom pairs in human readable format db.explain(apset[1]) ## Coerce APset to other objects apset2descdb(apset) # returns old list-style AP database tmp <- as(apset, "list") # returns list as(tmp, "APset") # converst list back to APset ## Compound similarity searching with APset cmp.search(apset, apset[1], type=3, cutoff=0.2) plot(sdfset[names(cmp.search(apset, apset[6], type=2, cutoff=0.4))]) ## Identify compounds with identical AP sets cmp.duplicated(apset, type=2) ## Structure similarity clustering cmp.cluster(db=apset, cutoff = c(0.65, 0.5))[1:20,]showClass("APset") ## Instance of SDFset class data(sdfsample) sdfset <- sdfsample[1:50] sdf <- sdfsample[[1]] ## Compute atom pair library ap <- sdf2ap(sdf) (apset <- sdf2ap(sdfset)) view(apset[1:4]) ## Return main components of APset object cid(apset[1:4]) # compound IDs ap(apset[1:4]) # atom pair descriptors ## Return atom pairs in human readable format db.explain(apset[1]) ## Coerce APset to other objects apset2descdb(apset) # returns old list-style AP database tmp <- as(apset, "list") # returns list as(tmp, "APset") # converst list back to APset ## Compound similarity searching with APset cmp.search(apset, apset[1], type=3, cutoff=0.2) plot(sdfset[names(cmp.search(apset, apset[6], type=2, cutoff=0.4))]) ## Identify compounds with identical AP sets cmp.duplicated(apset, type=2) ## Structure similarity clustering cmp.cluster(db=apset, cutoff = c(0.65, 0.5))[1:20,]
APset to list-style AP database
Coerces APset to old list-style descriptor database used by search/cluster functions.
apset2descdb(apset)apset2descdb(apset)
apset |
Object of class |
...
list |
with following components |
descdb |
list of atom pair sets |
cids |
compound IDs |
sdfsegs |
start/end coordinates for each molecule in SD file; only populated when |
source |
path/name of SD file |
type |
import method |
Thomas Girke
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", J Chem Inf Comput Sci.
Functions: SDF2apcmp, sdf2ap, cmp.search, cmp.similarity
## Instance of SDFset class data(sdfsample) sdfset <- sdfsample[1:50] sdf <- sdfsample[[1]] ## Compute atom pair library ap <- sdf2ap(sdf) (apset <- sdf2ap(sdfset)) view(apset[1:4]) ## Return main components of APset object cid(apset[1:4]) # compound IDs ap(apset[1:4]) # atom pair descriptors ## Return atom pairs in human readable format db.explain(apset[1]) ## Coerce APset to other objects apset2descdb(apset) # returns old list-style AP database tmp <- as(apset, "list") # returns list as(tmp, "APset") # converst list back to APset ## Compound similarity searching with APset cmp.search(apset, apset[1], type=3, cutoff=0.2) plot(sdfset[names(cmp.search(apset, apset[6], type=2, cutoff=0.4))]) ## Identify compounds with identical AP sets cmp.duplicated(apset, type=2) ## Structure similarity clustering cmp.cluster(db=apset, cutoff = c(0.65, 0.5))[1:20,]## Instance of SDFset class data(sdfsample) sdfset <- sdfsample[1:50] sdf <- sdfsample[[1]] ## Compute atom pair library ap <- sdf2ap(sdf) (apset <- sdf2ap(sdfset)) view(apset[1:4]) ## Return main components of APset object cid(apset[1:4]) # compound IDs ap(apset[1:4]) # atom pair descriptors ## Return atom pairs in human readable format db.explain(apset[1]) ## Coerce APset to other objects apset2descdb(apset) # returns old list-style AP database tmp <- as(apset, "list") # returns list as(tmp, "APset") # converst list back to APset ## Compound similarity searching with APset cmp.search(apset, apset[1], type=3, cutoff=0.2) plot(sdfset[names(cmp.search(apset, apset[6], type=2, cutoff=0.4))]) ## Identify compounds with identical AP sets cmp.duplicated(apset, type=2) ## Structure similarity clustering cmp.cluster(db=apset, cutoff = c(0.65, 0.5))[1:20,]
Returns atom block(s) from an object of class SDF or SDFset.
atomblock(x)atomblock(x)
x |
object of class |
...
matrix if SDF is provided or list of matrices if SDFset is provided
Thomas Girke
...
header, atomcount, bondblock, datablock, cid, sdfid
## SDF/SDFset instances data(sdfsample) sdfset <- sdfsample sdf <- sdfset[[1]] ## Extract atome block atomblock(sdf) atomblock(sdfset[1:4]) ## Replacement methods sdfset[[1]][[2]][1,1] <- 999 sdfset[[1]] atomblock(sdfset)[1:2] <- atomblock(sdfset)[3:4] atomblock(sdfset[[1]]) == atomblock(sdfset[[3]]) view(sdfset[1:2])## SDF/SDFset instances data(sdfsample) sdfset <- sdfsample sdf <- sdfset[[1]] ## Extract atome block atomblock(sdf) atomblock(sdfset[1:4]) ## Replacement methods sdfset[[1]][[2]][1,1] <- 999 sdfset[[1]] atomblock(sdfset)[1:2] <- atomblock(sdfset)[3:4] atomblock(sdfset[[1]]) == atomblock(sdfset[[3]]) view(sdfset[1:2])
Functions to compute molecular properties: weight, formula, atom frequencies, etc.
atomcount(x, addH = FALSE, ...) atomcountMA(x, ...) MW(x, mw=atomprop, ...) MF(x, ...)atomcount(x, addH = FALSE, ...) atomcountMA(x, ...) MW(x, mw=atomprop, ...) MF(x, ...)
x |
object of class |
mw |
|
addH |
'addH = TRUE' should be passed on to any of these function to add hydrogens that are often not specified in SD files |
... |
Arguments to be passed to/from other methods. |
...
named vector |
|
list |
|
matrix |
|
Thomas Girke
Standard atomic weights (2005) from: http://iupac.org/publications/pac/78/11/2051/
Functions: datablock, datablocktag
## Instance of SDFset class data(sdfsample) sdfset <- sdfsample ## Compute properties; to consider missing hydrogens, set 'addH = TRUE' MW(sdfset[1:4], addH = FALSE) MF(sdfset[1:4], addH = FALSE) atomcount(sdfset[1:4], addH = FALSE) propma <- atomcountMA(sdfset[1:4], addH = FALSE) boxplot(propma, main="Atom Frequency") ## Example for injecting a custom matrix/data frame into the data block of an ## SDFset and then writing it to an SD file props <- data.frame(MF=MF(sdfset), MW=MW(sdfset), atomcountMA(sdfset)) datablock(sdfset) <- props view(sdfset[1:4]) # write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE)## Instance of SDFset class data(sdfsample) sdfset <- sdfsample ## Compute properties; to consider missing hydrogens, set 'addH = TRUE' MW(sdfset[1:4], addH = FALSE) MF(sdfset[1:4], addH = FALSE) atomcount(sdfset[1:4], addH = FALSE) propma <- atomcountMA(sdfset[1:4], addH = FALSE) boxplot(propma, main="Atom Frequency") ## Example for injecting a custom matrix/data frame into the data block of an ## SDFset and then writing it to an SD file props <- data.frame(MF=MF(sdfset), MW=MW(sdfset), atomcountMA(sdfset)) datablock(sdfset) <- props view(sdfset[1:4]) # write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE)
Data frame with atom names, symbols, standard atomic weights, group number and period number.
data(atomprop)data(atomprop)
The format is a data frame with 117 rows and 6 columns.
Columns 1 to 4 from: http://iupac.org/publications/pac/78/11/2051/ Columns 5 to 6 from: http://en.wikipedia.org/wiki/List_of_elements
Pure Appl. Chem., 2006, Vol. 78, No. 11, pp. 2051-2066
data(atomprop) atomprop[1:4,]data(atomprop) atomprop[1:4,]
Function to obtain a substructure from SDF/SDFset objects by providing a row index for the atom block in an SDF referencing the atoms of interest. The function subsets both the atom and bond block(s) accordingly.
atomsubset(x, atomrows, type="new", datablock = FALSE)atomsubset(x, atomrows, type="new", datablock = FALSE)
x |
object of class |
atomrows |
The argument |
type |
The argument |
datablock |
By default the data block(s) in |
...
object of class SDF or SDFset
Thomas Girke
...
...
## Instance of SDFset class data(sdfsample) sdfset <- sdfsample ## Subset one or more molecules with atom index(es) to obtain substructure(s) atomsubset(sdfset[[1]], atomrows=1:18) indexlist <- list(1:18, 1:12) names(indexlist) <- cid(sdfset[1:2]) atomsubset(sdfset[1:2], atomrows=indexlist)## Instance of SDFset class data(sdfsample) sdfset <- sdfsample ## Subset one or more molecules with atom index(es) to obtain substructure(s) atomsubset(sdfset[[1]], atomrows=1:18) indexlist <- list(1:18, 1:12) names(indexlist) <- cid(sdfset[1:2]) atomsubset(sdfset[1:2], atomrows=indexlist)
When doing a select were the condition is a large number of ids it is not always possible to include them in a single SQL statement. This function will break the list of ids into chunks and allow the indexProcessor to deal with just a small number of ids.
batchByIndex(allIndices, indexProcessor, batchSize = 1e+05)batchByIndex(allIndices, indexProcessor, batchSize = 1e+05)
allIndices |
A vector of values that will be broken into batches and passed as an argument to the
|
indexProcessor |
A function that takes one batch if indices. It is called once for each batch. The return value from this function is ignored. To accumulate results you can write to a global variable using the "<<-" operator. |
batchSize |
The size of each batch. The last batch may be smaller than this value. |
No value is returned.
Kevin Horan
## Not run: result=NA indices = 1:10000 #run a query on each batch of indexes, appending each result to # "result" as we go. batchByIndex(indices, function(indexBatch){ df = dbGetQuery(dbConnection, generateQuery(indexBatch)) result <<- if(is.na(result)) df else rbind(result,df) },1000) ## End(Not run)## Not run: result=NA indices = 1:10000 #run a query on each batch of indexes, appending each result to # "result" as we go. batchByIndex(indices, function(indexBatch){ df = dbGetQuery(dbConnection, generateQuery(indexBatch)) result <<- if(is.na(result)) df else rbind(result,df) },1000) ## End(Not run)
Returns bond block(s) from an object of class SDF or SDFset.
bondblock(x)bondblock(x)
x |
object of class |
...
matrix if SDF is provided or list of matrices if SDFset is provided
Thomas Girke
...
header, atomcount, atomblock, datablock, cid, sdfid
## SDF/SDFset instances data(sdfsample) sdfset <- sdfsample sdf <- sdfset[[1]] ## Extract bond block bondblock(sdf) bondblock(sdfset[1:4]) ## Replacement methods sdfset[[1]][[3]][1,1] <- 999 sdfset[[1]] bondblock(sdfset)[1:2] <- bondblock(sdfset)[3:4] bondblock(sdfset[[1]]) == bondblock(sdfset[[3]]) view(sdfset[1:2])## SDF/SDFset instances data(sdfsample) sdfset <- sdfsample sdf <- sdfset[[1]] ## Extract bond block bondblock(sdf) bondblock(sdfset[1:4]) ## Replacement methods sdfset[[1]][[3]][1,1] <- 999 sdfset[[1]] bondblock(sdfset)[1:2] <- bondblock(sdfset)[3:4] bondblock(sdfset[[1]]) == bondblock(sdfset[[3]]) view(sdfset[1:2])
Returns information about bonds, charges and missing hydrogens in SDF and SDFset objects.
bonds(x, type = "bonds")bonds(x, type = "bonds")
x |
|
type |
If If |
It is used by many other functions (e.g. MW, MF, atomcount, atomcuntMA and plot) to correct for missing hydrogens that are often not specified in SD files.
If x is of class SDF, then a single data.frame or vector is returned. If x is of class SDFset, then a list of data.frames or vecotors is returned that has the same length and order as x.
Thomas Girke
...
Functions: conMA
Class: SDF and SDFset
## Instances of SDFset class data(sdfsample) sdfset <- sdfsample ## Returns data frames with bonds and charges bonds(sdfset[1:2], type="bonds") ## Returns charged atoms in each molecule bonds(sdfset[1:2], type="charge") ## Returns the number of missing hydrogens in each molecule bonds(sdfset[1:2], type="addNH")## Instances of SDFset class data(sdfsample) sdfset <- sdfsample ## Returns data frames with bonds and charges bonds(sdfset[1:2], type="bonds") ## Returns charged atoms in each molecule bonds(sdfset[1:2], type="charge") ## Returns the number of missing hydrogens in each molecule bonds(sdfset[1:2], type="addNH")
Launches a web browser to view the results of a ChemMine Tools web job with an interactive online viewer.
Note that this reassigns the job to the current logged in user within the browser, so it becomes no longer
accessible by the result and status functions. Any results should be saved within R before launching a browser.
browseJob(object)browseJob(object)
object |
A |
Returns an URL string which can be used to access the job results. The function also attempts to open the url with the browseURL function. As this URL can only be used once, the returned string is only useful if the browseURL function fails to open a browser.
Tyler William H Backman
See ChemMine Tools at http://chemmine.ucr.edu.
Functions: toolDetails, listCMTools, launchCMTool, result, status
## Not run: ## list available tools listCMTools() ## get detailed instructions on using a tool toolDetails("Fingerprint Search") ## download compound 2244 from PubChem job1 <- launchCMTool("pubchemID2SDF", 2244) ## check job status and download result status(job1) result1 <- result(job1) ## open job in web browser browseJob(job1) ## End(Not run)## Not run: ## list available tools listCMTools() ## get detailed instructions on using a tool toolDetails("Fingerprint Search") ## download compound 2244 from PubChem job1 <- launchCMTool("pubchemID2SDF", 2244) ## check job status and download result status(job1) result1 <- result(job1) ## open job in web browser browseJob(job1) ## End(Not run)
Buffer the input of files to increase efficiency
bufferLines(fh, batchSize, lineProcessor)bufferLines(fh, batchSize, lineProcessor)
fh |
file handle |
batchSize |
How many lines to read in each batch |
lineProcessor |
Each batch of lines will be passed to this function for processing |
No return value
Kevin Horan
## Not run: fh = file("filename") bufferLines(fh,100,function(lines) { message("found ",length(lines)," lines") }) ## End(Not run)## Not run: fh = file("filename") bufferLines(fh,100,function(lines) { message("found ",length(lines)," lines") }) ## End(Not run)
Allow query results to be processed in batches for efficiency.
bufferResultSet(rs, rsProcessor, batchSize = 1000,closeRS=FALSE)bufferResultSet(rs, rsProcessor, batchSize = 1000,closeRS=FALSE)
rs |
A DBIResult object, usually from |
rsProcessor |
Each batch will be passed as a data frame to this function for processing. |
batchSize |
The number of rows to read in each batch |
closeRS |
Should the result set be closed by this function when it is done? |
No value.
Kevin Horan
##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (rs, rsProcessor, batchSize = 1000) { while (TRUE) { chunk = fetch(rs, n = batchSize) if (dim(chunk)[1] == 0) break rsProcessor(chunk) } }##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (rs, rsProcessor, batchSize = 1000) { while (TRUE) { chunk = fetch(rs, n = batchSize) if (dim(chunk)[1] == 0) break rsProcessor(chunk) } }
Re-organize a vector valued clustering into an list which groups cluster members together
byCluster(clustering, excludeSingletons = TRUE)byCluster(clustering, excludeSingletons = TRUE)
clustering |
A named vector in which the names are cluster members and the values are cluster labels. This is format output by jarvisPatrick. |
excludeSingletons |
If true only clusters with more than 1 member will be in the output, otherwise all clusters will be used. |
A list with a slot for each cluster. Each slot of the list is a vector containing the cluster members.
Kevin Horan
data(apset) cl = jarvisPatrick(nearestNeighbors(apset,cutoff=0.6),k=2) print(byCluster(cl))data(apset) cl = jarvisPatrick(nearestNeighbors(apset,cutoff=0.6),k=2) print(byCluster(cl))
Canonicalizes the atom numbering of a compound. The implimentation of this function is in Open Babel and requires the ChemmineOB package to function.
canonicalize(sdf)canonicalize(sdf)
sdf |
Any sdfset object. |
A new SDFset in which all compounds have been canonicalized
Kevin Horan
http://openbabel.org/api/2.3/canonical_code_algorithm.shtml
## Not run: data(sdfsample) canonicalSdf = canonicalize(sdfsample[1]) ## End(Not run)## Not run: data(sdfsample) canonicalSdf = canonicalize(sdfsample[1]) ## End(Not run)
Computes a re-arrangement required to transform the atom numbering of the given compound into the canonical atom numbering. This function uses the OBGraphSym and CanonicalLabels classes of Open Babel to compute the re-arrangement.
canonicalNumbering(sdf)canonicalNumbering(sdf)
sdf |
Any sdfset object. |
A list of vectors of index values. Each item in the list corresponds to one of the given compounds. The values of a list item are the re-arrangement of the atoms. For example, if the value in item 1, column 1 is 25, that means that atom number 1 in the original compound should become atom number 25 in the canonical version of that compound.
Kevin Horan
http://openbabel.org/api/2.3/canonical_code_algorithm.shtml
## Not run: data(sdfsample) labels = canonicalNumbering(sdfsample[1]) ## End(Not run)## Not run: data(sdfsample) labels = canonicalNumbering(sdfsample[1]) ## End(Not run)
Returns the compound identifiers from the ID slot of an SDFset object.
cid(x)cid(x)
x |
object of class |
...
character vector
Thomas Girke
...
atomblock, atomcount, bondblock, datablock, header, sdfid
## SDFset/APset instances data(sdfsample) sdfset <- sdfsample apset <- sdf2ap(sdfset[1:4]) ## Extract compound IDs from SDFset/APset cid(sdfset[1:4]) cid(apset[1:4]) ## Extract IDs defined in SD file sdfid(sdfset[1:4]) ## Assigning compound IDs and keeping them unique unique_ids <- makeUnique(sdfid(sdfset)) cid(sdfset) <- unique_ids cid(sdfset[1:4]) ## Replacement Method cid(sdfset) <- as.character(1:100)## SDFset/APset instances data(sdfsample) sdfset <- sdfsample apset <- sdf2ap(sdfset[1:4]) ## Extract compound IDs from SDFset/APset cid(sdfset[1:4]) cid(apset[1:4]) ## Extract IDs defined in SD file sdfid(sdfset[1:4]) ## Assigning compound IDs and keeping them unique unique_ids <- makeUnique(sdfid(sdfset)) cid(sdfset) <- unique_ids cid(sdfset[1:4]) ## Replacement Method cid(sdfset) <- as.character(1:100)
'cluster.sizestat' is used to do simple statistics on sizes of clusters generated by 'cmp.cluster'. It will return a dataframe which maps a cluster size to the number of clusters with that size. It is often used along with 'cluster.visualize'.
cluster.sizestat(cls, cluster.result=1)cluster.sizestat(cls, cluster.result=1)
cls |
The clustering result returned by 'cmp.cluster' |
cluster.result |
If multiple cutoff values are used in clustering process, this argument tells which cutoff value is to be considered here. |
'cluster.sizestat' depends on the format that is returned by 'cmp.cluster' - it will treat the first column as the indecies, and the second column as the cluster sizes of effective clustering. Because of this, when multiple cutoffs are used when 'cmp.cluster' is called, 'cluster.sizestat' will only consider the clustering result of the first cutoff. If you want to work on an alternative cutoff, you have to manually reorder/remove columns.
Returns a data frame of two columns.
cluster size |
This column lists cluster sizes |
count |
This column lists number of clusters of a cluster size |
Y. Eddie Cao
cmp.cluster, cluster.visualize
## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) ## Binning clustering using variable similarity cutoffs. cluster <- cmp.cluster(db=apset, cutoff = c(0.65, 0.5)) ## Statistics on sizes of clusters cluster.sizestat(cluster[,c(1,2,3)]) cluster.sizestat(cluster[,c(1,4,5)])## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) ## Binning clustering using variable similarity cutoffs. cluster <- cmp.cluster(db=apset, cutoff = c(0.65, 0.5)) ## Statistics on sizes of clusters cluster.sizestat(cluster[,c(1,2,3)]) cluster.sizestat(cluster[,c(1,4,5)])
'cluster.visualize' takes clustering result returned by 'cmp.cluster' and generate multi-dimensional scaling plot for visualization purpose.
cluster.visualize(db, cls, size.cutoff, distmat=NULL, color.vector=NULL, non.interactive="", cluster.result=1, dimensions=2, quiet=FALSE, highlight.compounds=NULL, highlight.color=NULL, ...)cluster.visualize(db, cls, size.cutoff, distmat=NULL, color.vector=NULL, non.interactive="", cluster.result=1, dimensions=2, quiet=FALSE, highlight.compounds=NULL, highlight.color=NULL, ...)
db |
The desciptor database, in the format returned by 'cmp.parse'. |
cls |
The clustering result returned by 'cmp.cluster'. |
size.cutoff |
The cutoff size for clusters considered in this visualization. Clusters of size smaller than the cutoff will not be considered. |
distmat |
A distance matrix that corresponds to the 'db'. If not provided, it will be computed on-the-fly in an efficient manner. |
color.vector |
Colors to be used in the plot. If the number of colors in the vector is not enough for the plot, colors will be reused. If not provided, color will be generated and randomly sampled from 'rainbow'. |
non.interactive |
If provided, will enable the non-interactive mode, and the plot will be in an eps file named after this value. |
cluster.result |
Used to select the clustering result if multiple clustering results are present in 'cls'. |
dimensions |
Dimensionality to be used in visualization. See details. |
quiet |
Whether to supress the progress bar. |
highlight.compounds |
A vector of compound IDs, corresponding to compounds to be highlighted in the plot. A highlighted compound is represented as a filled circle. |
highlight.color |
Color used for highlighted compounds. If not set, a highlighted compounds will have the same color as that used for other compounds in the same cluster. |
... |
Further arguments will be passed to 'cmp.similarity' to calculate similarity matrix. |
'cluster.visualize' internally calls the 'cmdscale' function to generate a set of points in 2-D for the compounds in selected clusters. Note that for compounds in clusters smaller than the cutoff size, they will not be considered in this calculation - their entries in 'distmat' will be discarded if 'distmat' is provided, and distances involving them will not be computed if 'distmat' is not provided.
To determine the value for 'size.cutoff', you can use 'cluster.sizestat' to see the size distribution of clusters.
Because 'cmp.cluster' function allows you to perform multiple clustering processes simultaneously with different cutoff values, the 'cls' parameter may point to a data frame containing multiple clustering results. The user can use 'cluster.result' to specify which result to use. By default, this is set to 1, and the first clustering result will be used in visualization. Whatever the value is, in interactive mode (described below), all clustering result will be displayed when a compound is selected in the interactive plot.
If the colors provided in 'color.vector' are not enough to distinguish clusters by colors, the function will silently reuse the colors, resulting multiple clusters colored in the same color. We suggest you use 'cluster.sizestat' to see how many clusters will be selected using your 'size.cutoff', or simply provide no 'color.vector'.
If 'non.interative' is not set, the final plot is interactive. You will be able to select points by clicking them. When you click on any point, information about the compound represented by that point will be displayed. This includes the cluster ID, cluster size, compound index in the SDF and compound name if any. You can then perform another selection. To exit this process, right click on X11 device or press ESC in non-X11 device (Quartz and Windows).
By default, 'dimensions' is set to 2, and the built-in 'plot' function will be used for plotting. If you need to do 3-Dimensional plotting, set 'dimensions' to 3, and pass the returned value to 3D plot utilities, such as 'scatterplot3d' or 'rggobi'. This package does not perform 3D plot on its own.
This function returns a data frame of MDS coordinates and clustering result. This value can be passed to 3D plot utilities such as 'scatterplot3d' and 'rggobi'.
The last column of the output gives whether the compounds have been clicked in the interactive mode.
Y. Eddie Cao
cmp.parse, cmp.cluster, cluster.sizestat
## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) db <- apset ## cluster db with 2 cutoffs clusters <- cmp.cluster(db, cutoff=c(0.5, 0.4)) ## Return size stats sizestat <- cluster.sizestat(clusters) ## Visualize results, using a cutoff of 3, write to file 'test.eps' coord <- cluster.visualize(db, clusters, 2, non.interactive="test.eps") ## Not run: ## visualize it in interactive mode, using a cutoff of 3 and the 2nd clustering result coord <- cluster.visualize(db, clusters, cluster.result=2, 3) ## 3D visualization with scatterplot3d coord <- cluster.visualize(db, clusters, 3, dimensions=3) library(scatterplot3d) scatterplot3d(coord) ## End(Not run)## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) db <- apset ## cluster db with 2 cutoffs clusters <- cmp.cluster(db, cutoff=c(0.5, 0.4)) ## Return size stats sizestat <- cluster.sizestat(clusters) ## Visualize results, using a cutoff of 3, write to file 'test.eps' coord <- cluster.visualize(db, clusters, 2, non.interactive="test.eps") ## Not run: ## visualize it in interactive mode, using a cutoff of 3 and the 2nd clustering result coord <- cluster.visualize(db, clusters, cluster.result=2, 3) ## 3D visualization with scatterplot3d coord <- cluster.visualize(db, clusters, 3, dimensions=3) library(scatterplot3d) scatterplot3d(coord) ## End(Not run)
'cmp.cluster' uses structural compound descriptors and clusters the
compounds based on their pairwise distances. cmp.cluster uses
single linkage to measure distance between clusters when it
merges clusters. It accepts both a single cutoff and a
cutoff vector. By using a cutoff vector, it can generate results
similar to hierarchical clustering after tree cutting.
cmp.cluster(db, cutoff, is.similarity = TRUE, save.distances = FALSE, use.distances = NULL, quiet = FALSE, ...)cmp.cluster(db, cutoff, is.similarity = TRUE, save.distances = FALSE, use.distances = NULL, quiet = FALSE, ...)
db |
The desciptor database, in the format returned by 'cmp.parse'. |
cutoff |
The clustering cutoff. Can be a single value or a vector. The cutoff gives the maximum distance between two compounds in order to group them in the same cluster. |
is.similarity |
Set when the cutoff supplied is a similarity cutoff. This cutoff is the minimum similarity value between two compounds such that they will be grouped in the same cluster. |
save.distances |
whether to save distance for future clustering. See details below. |
use.distances |
Supply pre-computed distance matrix. |
quiet |
Whether to suppress the progress information. |
... |
Further arguments to be passed to |
cmp.cluster will compute distances on the fly if use.distances is not set.
Furthermore, if save.distances is not set, the distance values computed will never be
stored and any distance between two compounds is guaranteed not to be
computed twice. Using this method, cmp.cluster can deal with large databases
when a distance matrix in memory is not feasible. The speed of the clustering
function should be slowed when using a transient distance calculation.
When save.distances is set, cmp.cluster will be forced to compute the
distance matrix and save it in memory before the clustering. This is
useful when additional clusterings are required in the future without re-computed
the distance matrix. Set save.distances to TRUE if you
only want to force the clustering to use this 2-step approach; otherwise,
set it to the filename under which you want the distance matrix to be
saved. After you save it, when you need to reuse the distance matrix, you
can 'load' it, and supply it to cmp.cluster via the use.distances argument.
cmp.cluster supports a vector of several cutoffs. When you have multiple cutoffs,
cmp.cluster still guarantees that pairwise distances will never be
recomputed, and no copy of distances is kept in memory. It is guaranteed to
be as fast as calling cmp.cluster with a single cutoff that results in the
longest processing time, plus some small overhead linear in processing
time.
Returns a data.frame. Besides a variable giving compound ID, each of the
other variables in the data frame will either give the cluster IDs of
compounds under some clustering cutoff, or the size of clusters that the
compounds belong to. When N cutoffs are given, in total 2*N+1 variables
will be generated, with N of them giving the cluster ID of each compound
under each of the N cutoffs, and the other N of them giving the cluster
size under each of the N cutoffs. The rows are sorted by cluster sizes.
Y. Eddie Cao, Li-Chang Cheng
cmp.parse1, cmp.parse, cmp.search, cmp.similarity
## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads atom pair and atom pair fingerprint samples provided by library data(apset) db <- apset fpset <- desc2fp(apset) ## Clustering of 'APset' object with multiple cutoffs clusters <- cmp.cluster(db=apset, cutoff=c(0.5, 0.85)) ## Clustering of 'FPset' object with multiple cutoffs. This method allows to call ## various similarity methods provided by the fpSim function. clusters2 <- cmp.cluster(fpset, cutoff=c(0.5, 0.7), method="Tversky") ## Saves the distance matrix before clustering: clusters <- cmp.cluster(db, cutoff=0.65, save.distances="distmat.rda") # Later one reload the matrix and pass it the clustering function. load("distmat.rda") clusters <- cmp.cluster(db, cutoff=0.60, use.distances=distmat)## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads atom pair and atom pair fingerprint samples provided by library data(apset) db <- apset fpset <- desc2fp(apset) ## Clustering of 'APset' object with multiple cutoffs clusters <- cmp.cluster(db=apset, cutoff=c(0.5, 0.85)) ## Clustering of 'FPset' object with multiple cutoffs. This method allows to call ## various similarity methods provided by the fpSim function. clusters2 <- cmp.cluster(fpset, cutoff=c(0.5, 0.7), method="Tversky") ## Saves the distance matrix before clustering: clusters <- cmp.cluster(db, cutoff=0.65, save.distances="distmat.rda") # Later one reload the matrix and pass it the clustering function. load("distmat.rda") clusters <- cmp.cluster(db, cutoff=0.60, use.distances=distmat)
'cmp.duplicated' detects duplicated compounds from a descriptor database generated by 'cmp.parse'. Two compounds are said to duplicate each other when their descriptors are the same.
cmp.duplicated(db, sort = FALSE, type=1)cmp.duplicated(db, sort = FALSE, type=1)
db |
The desciptor database, in the format returned by 'cmp.parse'. |
sort |
Whether to sort the descriptors for a compound. See details. |
type |
Returns results as vector (type=1) or data frame (type=2). |
'cmp.duplicated' will take the descriptors in the descriptor database, concatenate all descriptors for the same compound into a string, and use this string as the identification of a compound. If two compounds share the same identification string, they are said to duplicate each other.
'cmp.duplicated' assume the the database passed in as argument to follow the format generated by 'cmp.parse'. That is, 'db' is a list, 'db$descdb' is a list, and each entry of 'db$descdb' is an array of numeric values that give descriptors for one compound.
By default, 'cmp.duplicated' will assume the descriptors for a compound is already sorted. That is each entry in 'db\$descdb' is a sorted array. This is true for database generated by 'cmp.parse'. If you generate the database using some other tools, you might want to enable sorting.
Returns a logic array, telling whether a compound in the database is a duplication of a compound appearing before this one. For example, if the i-th element of the array is TRUE, it means that the i-th compound in the database is a duplication of a compound listed before this compound in the database.
The returned array can be used to remove duplication. Simply use it to index the descriptor database.
If you are interested in what compound is duplicated, you can do a search in the database with cutoff set to 1.
Y. Eddie Cao
## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) db <- apset ## Manually create a duplication (here compound 1 and 10) db[10] <- db[1] ## Find duplication dup <- cmp.duplicated(db) dup cid(db[dup]) ## Remove all duplications db <- db[!dup]## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) db <- apset ## Manually create a duplication (here compound 1 and 10) db[10] <- db[1] ## Find duplication dup <- cmp.duplicated(db) dup cid(db[dup]) ## Remove all duplications db <- db[!dup]
'cmp.parse' will take a SDF file, parse all the compounds encoded, compute their atom-pair descriptors, and return the descriptors as a list. The list contains two names, 'descdb' and 'cids'. 'descdb' is a vector of descriptors, and 'cids' is a list of names of compounds found in the SDF file. The returned list is usually used to a database, against which similarity search can be performed using the 'search' function. These two functions will parse all compounds in the SDF file. To parse a single compound, use 'cmp.parse1' instead.
cmp.parse(filename)cmp.parse(filename)
filename |
The file name of the SDF file |
The 'filename' can be a local file or an URL. It is interactive, and will display the parsing progress. Since the parsing will also compute of atom-pair descriptors, it is time consuming. You will be reminded to save the parsing result for future use at the end of parsing.
'type' is either set to the default value 'normal' or 'file-backed'. When set to 'file-backed', the parsing work will be delegated to a separate package called 'ChemmineRpp', and the database will be stored in a file instead of in the primary memory. Therefore, 'file-backed' mode can handle larger compound libraries. In 'file-backed' mode, 'dbname' will be used to name the database file. A suffix '.cdb' will be appended to the given name.
The type of the database is transparent to other part of the package. For example, calling 'cmp.search' against a database in 'file-backed' mode will cause the package to load the descriptors from the database file progressively.
Return a list that can be used as the database against which similarity search can be performed. The 'search' and 'cmp.cluster' functions both expect a database returned by 'cmp.parse'.
descdb |
A vector containing the descriptors for all the compounds. |
cids |
Compound ID information found in the SDF file. It is the first line of SDF of a compound. |
Y. Eddie Cao, Li-Chang Cheng
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", in J Chem Inf Comput Sci.
cmp.parse1, cmp.search,
cmp.cluster,
cmp.similarity
## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) db <- apset # (optinally) save the db for future use save(db, file="db.rda", compress=TRUE) # ... # later, in a separate session, you can load it back: load("db.rda")## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) db <- apset # (optinally) save the db for future use save(db, file="db.rda", compress=TRUE) # ... # later, in a separate session, you can load it back: load("db.rda")
Read SDF information from an SDF file or connection, parse the first compound, and calculate the descriptor for that compound. The returned descriptor can be added to database returned by 'cmp.parse' or be used as the query structure when calling 'search'. This function will only parse one compound and return only the descriptor. To parse all compounds in an SDF file, use 'cmp.parse'.
cmp.parse1(filename)cmp.parse1(filename)
filename |
The file name of the SDF file or a URL or a connection. |
'cmp.parse1' can take a file name or a URL or a connection. When a connection is used, the current line must be the first line of SDF of the compound to be parsed. 'cmp.parse1' will skip the header and parse from the 4th line. Therefore, the compound ID information will be skipped. After the parsing is done, if 'filename' is a connection, it will then point to the line after the connection table of SDF. You can use some other procedure to parse the annotation block.
Return the descriptor, which is encoded as a vector.
Y. Eddie Cao, Li-Chang Cheng
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", in J Chem Inf Comput Sci.
cmp.parse, cmp.search, cmp.cluster,
cmp.similarity
# load an SDF file from web and parse it ## Not run: structure <- cmp.parse1("http://bioweb.ucr.edu/ChemMineV2/compound/Aurora/b32:NNQS2MBRHAZTI===/sdf")# load an SDF file from web and parse it ## Not run: structure <- cmp.parse1("http://bioweb.ucr.edu/ChemMineV2/compound/Aurora/b32:NNQS2MBRHAZTI===/sdf")
Given descriptor of a query compound and a database of compound descriptors, search for compounds that are similar to the query compound. User can limit the output by supplying a cutoff similarity score or a cutoff that limits the number of returned compounds. The function can also return the scores together with the compounds.
cmp.search(db, query, type=1, cutoff = 0.5, return.score = FALSE, quiet = FALSE, mode = 1,visualize = FALSE, visualize.browse = TRUE, visualize.query = NULL)cmp.search(db, query, type=1, cutoff = 0.5, return.score = FALSE, quiet = FALSE, mode = 1,visualize = FALSE, visualize.browse = TRUE, visualize.query = NULL)
db |
The compound descriptor database returned by 'cmp.parse'. |
query |
The query descriptor, which is usually returned by 'cmp.parse1'. |
type |
Returns results in form of position indices (type=1), named vector with compound IDs (type=2) or data frame (type=3). |
cutoff |
The cutoff similarity (when cutoff <= 1) or the number of maximum compounds to be returned (when cutoff > 1). |
return.score |
Whether to return similarity scores. If set to TRUE, a data frame will be returned; otherwise, only the compounds' indices in the database will be returned in the order of decreasing scores. |
quiet |
Whether to disable progress information. |
mode |
Mode used when computing similarity scores. This value is passed to 'cmp.similarity'. |
visualize |
|
visualize.browse |
|
visualize.query |
'cmp.search' will go through all the compound descriptors in the database and calculate the similarity between the query compound and compounds in the database. When cutoff similarity score is set, compounds having a similarity score higher than the cutoff will be returned. When maximum number of compounds to return is set to N via 'cutoff', the compounds having the highest N similarity scores will be returned.
When 'return.score' is set to FALSE, a vector of matching compounds' indices in the database will be returned. Otherwise, a data frame will be returned:
ids |
The indices of matching compounds in the database. |
scores |
The similarity scores between the matching compounds and the query compound |
Y. Eddie Cao, Li-Chang Cheng
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", in J Chem Inf Comput Sci.
cmp.parse1, cmp.parse,
cmp.search, cmp.cluster,
cmp.similarity, sdf.visualize
## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) db <- apset query <- db[1] ## Ooptinally, save the db for future use save(db, file="db.rda", compress=TRUE) ## Search for similar compounds using similarity cutoff cmp.search(db, query, cutoff=0.2, type=1) # returns index cmp.search(db, query, cutoff=0.2, type=2) # returns named vector cmp.search(db, query, cutoff=0.2, type=3) # returns data frame ## in the next session, you may use load a saved db and do the search: load("db.rda") cmp.search(db, query, cutoff=3) ## you may also use the loaded db to do clustering: cmp.cluster(db, cutoff=0.35)## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) db <- apset query <- db[1] ## Ooptinally, save the db for future use save(db, file="db.rda", compress=TRUE) ## Search for similar compounds using similarity cutoff cmp.search(db, query, cutoff=0.2, type=1) # returns index cmp.search(db, query, cutoff=0.2, type=2) # returns named vector cmp.search(db, query, cutoff=0.2, type=3) # returns data frame ## in the next session, you may use load a saved db and do the search: load("db.rda") cmp.search(db, query, cutoff=3) ## you may also use the loaded db to do clustering: cmp.cluster(db, cutoff=0.35)
Given descriptors for two compounds, 'cmp.similarity' returns the similarity measure between the two compounds.
cmp.similarity(a, b, mode = 1, worst = 0)cmp.similarity(a, b, mode = 1, worst = 0)
a |
Descriptor of the first compound. |
b |
Descriptor of the second compound. |
mode |
Mode used when computing the distance. See details below. |
worst |
The worst value you are expecting. If 'cmp.similarity' finds the upper bound of similarity is worse than it, it will return a 0 and potentially save some computation. |
'cmp.similarity' uses descriptor information generated by 'cmp.parse' and 'cmp.parse1'. Basically, a descriptor is a vector of numbers. The vector actually reprsents the set of descriptors of structural fragment. Similarity measurement uses Tanimoto coefficient.
'cmp.similarity' supports 3 different modes. In mode 1, normal Tanimoto coefficient is used. In mode 2, it uses the size of descriptor intersection over the size of the smaller descriptor, mainly to deal with compounds that vary a lot in size. In mode 3, it is similar to mode 2, except that it raises the similarity to the power 3 to penalize small values. When mode is 0, 'cmp.similarity' will select mode 1 or mode 3, based on the size differences between the two descriptors.
When 'cmp.similarity' is used in searching compounds with a threshold similarity value, or in clustering with a cutoff distance, the threshold similarity and cutoff distance can be used to decide a 'worse' value. 'cmp.similarity' can compute an upper bound of similarity easier, and by comparing this upper bound to the 'worst' value, it can potentially skip the real computation if it finds the similarity will be below the 'worst' value and will be useless to the caller.
Return a numeric value between 0 and 1 which gives the similarity between the two compounds.
Y. Eddie Cao, Li-Chang Cheng
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", in J Chem Inf Comput Sci.
Peter Willett (1998). "Chemical Similarity Searching", in J. Chem. Inf. Comput. Sci.
cmp.parse1, cmp.parse,
cmp.search, cmp.cluster
## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) ## Compute similarities among two compounds cmp.similarity(apset[1], apset[2]) ## Search apset database with a query compound cmp.search(apset, apset[1], type=3, cutoff = 0.3)## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) ## Compute similarities among two compounds cmp.similarity(apset[1], apset[2]) ## Search apset database with a query compound cmp.search(apset, apset[1], type=3, cutoff = 0.3)
Creates a bond matrix from SDF and SDFset objects. The matrix contains the atom labels in the row and column titles and the bond types are given in the data part as follows: 0 is no connection, 1 is a single bond, 2 is a double bond and 3 is a triple bond.
conMA(x, exclude = "none")conMA(x, exclude = "none")
x |
|
exclude |
if |
...
If x is of class SDF, then a single bond matrix is returned. If x is of class SDFset, then a list of matrices is returned that has the same length as x.
Thomas Girke
...
Functions: bonds
Class: SDF and SDFset
## Instances of SDFset class data(sdfsample) sdfset <- sdfsample ## Create bond matrix for first two molecules in sdfset conMA(sdfset[1:2], exclude=c("H")) ## Return bond matrix for first molecule and plot its structure with atom numbering conMA(sdfset[[1]], exclude=c("H")) plot(sdfset[1], atomnum = TRUE, noHbonds=FALSE , no_print_atoms = "", atomcex=0.8) ## Return number of non-H bonds for each atom rowSums(conMA(sdfset[[1]], exclude=c("H")))## Instances of SDFset class data(sdfsample) sdfset <- sdfsample ## Create bond matrix for first two molecules in sdfset conMA(sdfset[1:2], exclude=c("H")) ## Return bond matrix for first molecule and plot its structure with atom numbering conMA(sdfset[[1]], exclude=c("H")) plot(sdfset[1], atomnum = TRUE, noHbonds=FALSE , no_print_atoms = "", atomcex=0.8) ## Return number of non-H bonds for each atom rowSums(conMA(sdfset[[1]], exclude=c("H")))
Get a connection to one of the pre-build compound databases. The DrugBank database is distributed in the ChemmineDrugs package.
The DUD database will be downloaded the first time it is called.
It will download a 1.8GB zipped file which will expand to abut 9GB.
A directory to store the database in can be passed to the DUD() function.
DUD(destinationDir=".") DrugBank()DUD(destinationDir=".") DrugBank()
destinationDir |
The directory to store the downloaded DUD database in. |
A connection object to the ether the DUD or DrugBank database. This object must be passed to other functions which make use of the connection.
Kevin Horan
dbConn = DrugBank()dbConn = DrugBank()
Returns data block(s) from an object of class SDF or SDFset.
datablock(x) datablocktag(x, tag)datablock(x) datablocktag(x, tag)
x |
object of class |
tag |
|
...
named character vector if SDF is provided or list of named character vectors if SDFset is provided
Thomas Girke
...
atomblock, atomcount, bondblock, header, cid, sdfid
## SDF/SDFset instances data(sdfsample) sdfset <- sdfsample sdf <- sdfset[[1]] ## Extract data block datablock(sdf) datablock(sdfset[1:4]) datablocktag(sdfset, tag="PUBCHEM_OPENEYE_CAN_SMILES") ## Replacement methods sdfset[[1]][[1]][1] <- "test" sdfset[[1]] datablock(sdfset)[1] <- datablock(sdfset[2]) view(sdfset[1:2]) ## Example for injecting a custom matrix/data frame into the data block of an ## SDFset and then writing it to an SD file props <- data.frame(MF=MF(sdfset), MW=MW(sdfset), atomcountMA(sdfset)) datablock(sdfset) <- props view(sdfset[1:4]) # write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE)## SDF/SDFset instances data(sdfsample) sdfset <- sdfsample sdf <- sdfset[[1]] ## Extract data block datablock(sdf) datablock(sdfset[1:4]) datablocktag(sdfset, tag="PUBCHEM_OPENEYE_CAN_SMILES") ## Replacement methods sdfset[[1]][[1]][1] <- "test" sdfset[[1]] datablock(sdfset)[1] <- datablock(sdfset[2]) view(sdfset[1:2]) ## Example for injecting a custom matrix/data frame into the data block of an ## SDFset and then writing it to an SD file props <- data.frame(MF=MF(sdfset), MW=MW(sdfset), atomcountMA(sdfset)) datablock(sdfset) <- props view(sdfset[1:4]) # write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE)
Convert data blocks in SDFset to character matrix with
datablock2ma, then store its numeric columns as numeric matrix and its
character columns as character matrix.
datablock2ma(datablocklist, cleanup = " \\(.*", ...) splitNumChar(blockmatrix)datablock2ma(datablocklist, cleanup = " \\(.*", ...) splitNumChar(blockmatrix)
datablocklist |
|
blockmatrix |
|
cleanup |
|
... |
option to pass on additional arguments |
...
datablock2ma |
|
splitNumChar |
|
Thomas Girke
...
Classes: SDFset
## SDFset instance data(sdfsample) sdfset <- sdfsample # Convert data block to matrix blockmatrix <- datablock2ma(datablocklist=datablock(sdfset)) blockmatrix[1:4, 1:4] # Split matrix to numeric matrix and character matrix numchar <- splitNumChar(blockmatrix=blockmatrix) names(numchar) numchar[[1]][1:4,] numchar[[2]][1:4,]## SDFset instance data(sdfsample) sdfset <- sdfsample # Convert data block to matrix blockmatrix <- datablock2ma(datablocklist=datablock(sdfset)) blockmatrix[1:4, 1:4] # Split matrix to numeric matrix and character matrix numchar <- splitNumChar(blockmatrix=blockmatrix) names(numchar) numchar[[1]][1:4,] numchar[[2]][1:4,]
'db.explain' will take an atom-pair descriptor in numeric or a set of such descriptors, and interpret what they represent in a more human readable way.
db.explain(desc)db.explain(desc)
desc |
The descriptor or the array/vector of descriptors |
'desc' can be a single numeric giving a single descriptor or can be any container data type, such as vector or array, such that 'length(desc)' returns 2 or larger.
Return a character vector describing the descriptors.
## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) db <- apset ## Return atom pairs of first compound in human readable format db.explain(db[1])## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) db <- apset ## Return atom pairs of first compound in human readable format db.explain(db[1])
'db.subset' will take a descriptor database generated by 'cmp.parse' and an
array of indecies, and return a new database for compounds
corresponding to these indecies. The returned value is a descriptor database as returned by the cmp.parse function.
db.subset(db, cmps)db.subset(db, cmps)
db |
The database generated by 'cmp.parse' |
cmps |
An array of indecies that correspond to a set of selected compounds from the database |
'db.subset' creates a sub-database from 'db' by only including infomration that is relevant to compounds indexed by 'cmps'.
Return a descriptor database for the selected compounds. The format of the database is compatible with the one returned by cmp.parse.
## Note: this functionality has become obsolete since the introduction of the ## 'apset' S4 class. ## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) db <- apset olddb <- apset2descdb(db) ## Create a sub-database for the 1st and 2nd compound in that SDF db_sub <- db.subset(olddb, c(1, 2))## Note: this functionality has become obsolete since the introduction of the ## 'apset' S4 class. ## Load sample SD file # data(sdfsample); sdfset <- sdfsample ## Generate atom pair descriptor database for searching # apset <- sdf2ap(sdfset) ## Loads same atom pair sample data set provided by library data(apset) db <- apset olddb <- apset2descdb(db) ## Create a sub-database for the 1st and 2nd compound in that SDF db_sub <- db.subset(olddb, c(1, 2))
Run any db statements inside a transaction. If any error is raised the transaction will be rolled back, otherwise it will be committed at the end.
dbTransaction(conn, expr)dbTransaction(conn, expr)
conn |
A database connection object, such as is returned by |
expr |
Any block of code. |
The value of the given block of code will be returned upon successfully commiting the transaction. Otherwise an error will be raised.
Kevin Horan
conn = initDb("test15.db") dbTransaction(conn,{ # any db code here })conn = initDb("test15.db") dbTransaction(conn,{ # any db code here })
Generates fingerprints from descriptor vectors such as atom pairs stored in APset or list containers. The obtained fingerprints can be used for structure similarity comparisons, searching and clustering. Due to their compact size, computations on fingerprints are often more time and memory efficient than on their much more complex atom pair counterparts.
desc2fp(x, descnames=1024, type = "FPset")desc2fp(x, descnames=1024, type = "FPset")
x |
Object of classe |
descnames |
Descriptor set to consider for fingerprint encoding. If a single value from 1-4096 is provided then the function uses the corresponding number of the most frequent atom pairs stored in the |
type |
return fingerprint set as |
...
matrix or character vectors
Thomas Girke
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", J Chem Inf Comput Sci.
Functions: sdf2ap, SDF2apcmp, apset2descdb, cmp.search, cmp.similarity
Related classes: SDF, SDFset, SDFstr, APset.
## Instance of SDFset class data(sdfsample) sdfset <- sdfsample[1:10] ## Compute atom pair library apset <- sdf2ap(sdfset) ## Compute atom pair fingerprint matrix using internal atom pair ## selection containing 4096 most common atom pairs in DrugBank. ## For details see ?apfp. The following example uses from this ## set the 1024 most frequent atom pairs: fpset <- desc2fp(x=apset, descnames=1024, type="FPset") ## Alternatively, one can provide any custom atom pair selection. Here ## 1024 most common ones in apset object. fpset1024 <- names(rev(sort(table(unlist(as(apset, "list")))))[1:1024]) fpset2 <- desc2fp(x=apset, descnames=fpset1024, type="FPset") ## A more compact way of storing fingerprints is as character values fpchar <- desc2fp(x=apset, descnames=1024, type="character") ## Convert character fingerprints back to FPset or matrix fpset <- as(fpchar, "FPset") fpma <- as.matrix(fpset) ## Similarity searching returning Tanimoto similarity coefficients fpSim(x=fpset[1], y=fpset) ## Clustering example simMAap <- sapply(cid(fpset), function(x) fpSim(x=fpset[x], fpset, sorted=FALSE)) hc <- hclust(as.dist(1-simMAap), method="single") plot(as.dendrogram(hc), edgePar=list(col=4, lwd=2), horiz=TRUE)## Instance of SDFset class data(sdfsample) sdfset <- sdfsample[1:10] ## Compute atom pair library apset <- sdf2ap(sdfset) ## Compute atom pair fingerprint matrix using internal atom pair ## selection containing 4096 most common atom pairs in DrugBank. ## For details see ?apfp. The following example uses from this ## set the 1024 most frequent atom pairs: fpset <- desc2fp(x=apset, descnames=1024, type="FPset") ## Alternatively, one can provide any custom atom pair selection. Here ## 1024 most common ones in apset object. fpset1024 <- names(rev(sort(table(unlist(as(apset, "list")))))[1:1024]) fpset2 <- desc2fp(x=apset, descnames=fpset1024, type="FPset") ## A more compact way of storing fingerprints is as character values fpchar <- desc2fp(x=apset, descnames=1024, type="character") ## Convert character fingerprints back to FPset or matrix fpset <- as(fpchar, "FPset") fpma <- as.matrix(fpset) ## Similarity searching returning Tanimoto similarity coefficients fpSim(x=fpset[1], y=fpset) ## Clustering example simMAap <- sapply(cid(fpset), function(x) fpSim(x=fpset[x], fpset, sorted=FALSE)) hc <- hclust(as.dist(1-simMAap), method="single") plot(as.dendrogram(hc), edgePar=list(col=4, lwd=2), horiz=TRUE)
Draws an sdf object in the 2D plane using ggplot2 library. Permits customization of bond colors and atom colors.
draw_sdf(sdf, filename = "test.jpg", alpha_edge = 0.5, alpha_node = 1, numbered = FALSE, font_size = 5, node_vertical_offset = 0, node_background_color = FALSE, bgcolor = rgb(1, 1, 1, 1), bgraster = NULL, node_policy = default_node_policy(), edge_policy = default_edge_policy(), bond_dist_offset = 0.05, fmcsR_sdf = NULL)draw_sdf(sdf, filename = "test.jpg", alpha_edge = 0.5, alpha_node = 1, numbered = FALSE, font_size = 5, node_vertical_offset = 0, node_background_color = FALSE, bgcolor = rgb(1, 1, 1, 1), bgraster = NULL, node_policy = default_node_policy(), edge_policy = default_edge_policy(), bond_dist_offset = 0.05, fmcsR_sdf = NULL)
sdf |
An instance of a SDF or list of SDFs |
filename |
Filename to save image to. Defaults to 'test.jpg'. If set to NULL, does not save image. |
alpha_edge |
alpha of bonds in your image. Defaults to 0.5. 0 is fully transparent, 1 is fully opaque. |
alpha_node |
alpha of atoms in your image. Defaults to 1.0. |
numbered |
If 1 or TRUE, displays numbering of atoms at their location. If 2, displays a second numbering style. |
font_size |
Controls size of text to be displayed at atom locations. Beware when plotting multiple SDFs in one image. Ggplot will still scale fonts as if text is being plotted in one image. |
node_vertical_offset |
Upward shift of atom text. Upward shit is in SDF units, not ggplot units. |
bgcolor |
An rgb(r,g,b,alpha) or similar object. produces a background of the specified color. |
node_background_color |
A common color as a text string (e.g. 'white', 'pink') or an rgb(r,g,b,alpha). Draws a filled circle of the color specified before drawing text over each node. |
bgraster |
A readPNG object or a path to an object that can be understood using readPNG. Will be used as background. |
node_policy |
Mapping that defines how atom strings should be displayed. Simplest would be c('default'='black') |
edge_policy |
Mapping that defines how bonds should be displayed. Simplest is c('default'='black'), though this will display all Hydrogen bonds as well. |
bond_dist_offset |
Defines space between double or triple bonds, in SDF units. |
fmcsR_sdf |
A second SDF object to run fmcsR on. |
Requires ggplot2. Additional features require grid, gridExtra, fmcsR, or png. Most matrix operations vectorized.
Returns a ggplot2 object. Calling draw_sdf(...) rather than assigning it will result in R trying to print a ggplot2 object.
John A. Sharifi
library(ChemmineR) # if not already imported data(sdfsample) draw_sdf(sdfsample[[1]])library(ChemmineR) # if not already imported data(sdfsample) draw_sdf(sdfsample[[1]])
Computes the exact mass of each compound given.
exactMassOB(sdfset)exactMassOB(sdfset)
sdfset |
Any SDFset object. |
A vector of mass values.
Kevin Horan
## Not run: library(ChemmineR) data(sdfsample) mass = exactMassOB(sdfsample) ## End(Not run)## Not run: library(ChemmineR) data(sdfsample) mass = exactMassOB(sdfsample) ## End(Not run)
This is a subclass of SDF and thus inherits all the
slots and methods from that class. It adds a list of extended attributes
for atoms and bonds. These attributes can curretnly only be populated
from a V3000 formatted SDF file.
Objects can be created by calls of the form new("ExtSDF", ...). The
function read.SDFset will also return objects of this class if
the argument extendedAttributes is set to "TRUE".
extendedAtomAttributes:Object of class "list"
extendedBondAttributes:Object of class "list"
signature(x = "ExtSDF",atomId,tag): Returns the value of the given tag on the given atom number
signature(x = "ExtSDF",bondId,tag): Returns the value of the given tag on the given bond number
signature(object = "ExtSDF"): prints summary of SDF as well as any defined extended
attributes for the atoms or bonds
Kevin Horan
SDF V3000 format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Related classes: SDF, SDFset, SDFstr, AP, APset
showClass("ExtSDF")showClass("ExtSDF")
Searches the SQL database using features computed at load time. Each feature
used should be specified in the featureNames parameter. Then a set of filters
can be given to search for specific compounds.
findCompounds(conn, featureNames, tests)findCompounds(conn, featureNames, tests)
conn |
A database connection object, such as is returned by |
featureNames |
A list of all feature names used in any test. |
tests |
A vector of filters that must all be true for a compound to be returned. For example: c("MW <= 400","RINGS > 3") would return all compounds with a molecular weight of 400 or less and a more than 3 rings, assuming these features exist in the database. The syntax for each test is "<feature name> <SQL operator> <value>". These tests will simply be concatenated together with " AND " in-between them and tacked on the end of a WHERE clause of an SQL statement. So any SQL that will work in that context is fine. |
Returns a list of compound ids. The actual compounds can be fetched with getCompounds.
Kevin Horan
#create and initialize a new SQLite database conn = initDb("test1.db") data(sdfsample) #load data and compute 3 features: molecular weight, with the MW function, # and counts for RINGS and AROMATIC, as computed by rings, which returns a data frame itself. ids=loadSdf(conn,sdfsample, function(sdfset) data.frame(MW = MW(sdfset), rings(sdfset,type="count",upper=6, arom=TRUE)) ) #search for compounds with molecular weight less than 200 lightIds = findCompounds(conn,"MW",c("MW < 200")) MW(getCompounds(conn,lightIds)) # should find one compound with weight 140 unlink("test1.db")#create and initialize a new SQLite database conn = initDb("test1.db") data(sdfsample) #load data and compute 3 features: molecular weight, with the MW function, # and counts for RINGS and AROMATIC, as computed by rings, which returns a data frame itself. ids=loadSdf(conn,sdfsample, function(sdfset) data.frame(MW = MW(sdfset), rings(sdfset,type="count",upper=6, arom=TRUE)) ) #search for compounds with molecular weight less than 200 lightIds = findCompounds(conn,"MW",c("MW < 200")) MW(getCompounds(conn,lightIds)) # should find one compound with weight 140 unlink("test1.db")
Find the ids of compounds given the names.
findCompoundsByName(conn, names, keepOrder = FALSE, allowMissing = FALSE)findCompoundsByName(conn, names, keepOrder = FALSE, allowMissing = FALSE)
conn |
A database connection object, such as is returned by |
names |
A list of names of compounds to search for. The names are those that would be
returned by |
keepOrder |
If true, the order of the output compound ids will be the same as the input names. This imposes a performance hit that can be significant for large datasets, thus it should be left FALSE unless needed. |
allowMissing |
When this is false an error will be raised when names queried were not found in the database. If true, just those that are found will be returned with no error or warning. |
Returns the compound ids for compounds with the given name. The output order is not guaranteed unless keepOrder is set to TRUE. An error will be raised if any name cannot be found.
Kevin Horan
#create and initialize a new SQLite database conn = initDb("test4.db") data(sdfsample) #just load the data with no features or descriptors ids=loadSdf(conn,sdfsample) # find id of compound 650003 findCompoundsByName(conn,c("650003")) unlink("test4.db")#create and initialize a new SQLite database conn = initDb("test4.db") data(sdfsample) #just load the data with no features or descriptors ids=loadSdf(conn,sdfsample) # find id of compound 650003 findCompoundsByName(conn,c("650003")) unlink("test4.db")
Generates fingerprints from SDFsets using OpenBabel. The name of the fingerprint can also be set and can be anything available through OpenBabel. You can see what this list is by executing "obabel -L fingerprints". Results are returned as an FPset.
fingerprintOB(sdfSet, fingerprintName)fingerprintOB(sdfSet, fingerprintName)
sdfSet |
Input compounds to generate fingerprints for. |
fingerprintName |
The name of the fingerprint in Open Babel. A list of available names can be found by executing "obabel -L fingerprints". Currently that list is: "FP2", "FP3", "FP4", and "MACCS". |
An FPset with an element for each given compound.
Kevin Horan
## Not run: data(sdfsample) fpset = fingerprintOB(sdfsample) ## End(Not run)## Not run: data(sdfsample) fpset = fingerprintOB(sdfsample) ## End(Not run)
Fold a fingerprint. This takes the second half of the fingerprints and combines with the first half with a logical 'OR' operation. The result is a fingerprint with half as many bits.
fold(x, count = 1, bits = NULL)fold(x, count = 1, bits = NULL)
x |
The fingerprint(s) to fold. This can be either an |
count |
The number of times to fold this fingerprint. Folding will stop early if the fingerprint is reduced down to 1 bit before reaching the requested fold count. |
bits |
Fold this fingerprint until it is |
The new, folded, fingerprint.
Kevin Horan
fp = new("FP",fp=c(1,0,1,1, 0,0,1,0)) foldedFp = fold(fp,bits=4)fp = new("FP",fp=c(1,0,1,1, 0,0,1,0)) foldedFp = fold(fp,bits=4)
Returns the number of times this fingerprint has been folded.
foldCount(x)foldCount(x)
x |
Either an |
Returns the number of times this fingerprint has been folded.
Kevin Horan
fp = new("FP",fp=c(1,0,1,1, 0,0,1,0)) foldedFp=fold(fp) fc = foldCount(foldedFp) # == 1fp = new("FP",fp=c(1,0,1,1, 0,0,1,0)) foldedFp=fold(fp) fc = foldCount(foldedFp) # == 1
"FP"
Container for storing the fingerprint of a single compound. The FPset class is used for storing the fingerprints of many compounds.
Objects can be created by calls of the form new("FP", ...).
fp:Object of class "numeric"
foldCount:Object of class "numeric"
type:Object of class "character"
signature(x = "FP"): returns fingerprint as character string
signature(x = "FP"): returns fingerprint as numeric vector
signature(x = "FP"): returns fingerprint as numeric vector
signature(from = "FPset", to = "FP"): coerce FPset component to list with many FP objects
signature(from = "numeric", to = "FP"): construct FP object from numeric vector
signature(object = "FP"): prints summary of FP
signature(x = "FP"): concatenates any number of FP objects
signature(x = "FP"): fold fingerprint in half
signature(x = "FP"): number of times this object has been folded
signature(x = "FP"): the type of this fingerprint
signature(x = "FP"): the number of bits in this fingerprint
Thomas Girke
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", in J Chem Inf Comput Sci.
Related classes: SDF, SDFset, SDFstr, AP, APset, FPset.
showClass("FP") ## Instance of FP class data(apset) fpset <- desc2fp(apset) (fp <- fpset[[1]]) ## Class usage fpc <- as.character(fp) fpn <- as.numeric(fp) as(fpn, "FP") as(fpset[1:4], "FP")showClass("FP") ## Instance of FP class data(apset) fpset <- desc2fp(apset) (fp <- fpset[[1]]) ## Class usage fpc <- as.character(fp) fpn <- as.numeric(fp) as(fpn, "FP") as(fpset[1:4], "FP")
The function converts the base 64 encoded PubChem fingerprints to a binary matrix or a character vector. If applied to a SDFset object, then its data block needs to contain the PubChem fingerprint information.
fp2bit(x, type = 3, fptag = "PUBCHEM_CACTVS_SUBSKEYS")fp2bit(x, type = 3, fptag = "PUBCHEM_CACTVS_SUBSKEYS")
x |
Object of class |
type |
If set to |
fptag |
Name tag in SDF data block where the PubChem fingerprints are stored. Default is set to "PUBCHEM_CACTVS_SUBSKEYS". |
...
matrix, character or FPset
Thomas Girke
See PubChem fingerprint specification at: ftp://ftp.ncbi.nih.gov/pubchem/specifications/pubchem_fingerprints.txt
Functions: fpSim
## Load PubChem SDFset sample data(sdfsample); sdfset <- sdfsample cid(sdfset) <- sdfid(sdfset) ## Convert base 64 encoded fingerprints to FPset object fpset <- fp2bit(sdfset) ## Pairwise compound structure comparisons fpSim(fpset[1], fpset[2]) ## Structure similarity searching: x is query and y is fingerprint database fpSim(x=fpset[1], y=fpset, method="Tanimoto", cutoff=0, top="all") ## Compute fingerprint based Tanimoto similarity matrix simMA <- sapply(cid(fpset), function(x) fpSim(x=fpset[x], fpset, sorted=FALSE)) ## Hierarchical clustering with simMA as input hc <- hclust(as.dist(1-simMA), method="single") ## Plot hierarchical clustering tree plot(as.dendrogram(hc), edgePar=list(col=4, lwd=2), horiz=TRUE)## Load PubChem SDFset sample data(sdfsample); sdfset <- sdfsample cid(sdfset) <- sdfid(sdfset) ## Convert base 64 encoded fingerprints to FPset object fpset <- fp2bit(sdfset) ## Pairwise compound structure comparisons fpSim(fpset[1], fpset[2]) ## Structure similarity searching: x is query and y is fingerprint database fpSim(x=fpset[1], y=fpset, method="Tanimoto", cutoff=0, top="all") ## Compute fingerprint based Tanimoto similarity matrix simMA <- sapply(cid(fpset), function(x) fpSim(x=fpset[x], fpset, sorted=FALSE)) ## Hierarchical clustering with simMA as input hc <- hclust(as.dist(1-simMA), method="single") ## Plot hierarchical clustering tree plot(as.dendrogram(hc), edgePar=list(col=4, lwd=2), horiz=TRUE)
"FPset"
Container for storing fingerprints of many compounds. This container is used for structure similarity searching of compounds.
Objects can be created by calls of the form new("FPset", ...).
fpma:Object of class "matrix" with compound identifiers stored in row names
foldCount:Object of class "numeric"
type:Object of class "character"
signature(x = "FPset"): subsetting of class with bracket operator
signature(x = "FPset"): returns single component as FP object
signature(x = "FPset"): replacement method for several components
signature(x = "FPset"): returns content as named character vector
signature(x = "FPset"): returns content as numeric matrix
signature(x = "FPset"): concatenates any number of FPset containers
signature(x = "FPset"): returns all compound identifiers from row names
signature(x = "FPset"): replacement method for compound identifiers
signature(from = "FPset", to = "FP"): as(fpset, "FP")
signature(from = "matrix", to = "FPset"): as(fpma, "FPset")
signature(from = "character", to = "FPset"): as(fpchar, "FPset")
signature(x = "FPset"): returns number of entries stored in object
signature(object = "FPset"): prints summary of FPset
signature(x = "FPset"): prints extended summary of FPset
signature(x = "FPset"): fold fingerprint in half
signature(x = "FPset"): number of times this object has been folded
signature(x = "FPset"): the type of these fingerprints
signature(x = "FPset"): the number of bits in these fingerprints
Thomas Girke
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", in J Chem Inf Comput Sci.
Related classes: SDF, SDFset, SDFstr, AP, APset, FP.
showClass("FPset") ## Instance of FPset class data(apset) (fpset <- desc2fp(apset)) view(fpset) ## Class usage fpset[1:4] # behaves like a list fpset[[1]] # returns FP object length(fpset) # number of compounds cid(fpset) # returns compound ids fpset[1] <- 0 # replacement cid(fpset) <- 1:length(fpset) # replaces compound ids c(fpset[1:4], fpset[11:14]) # concatenation ## Coerce FPset from/to other objects fpma <- as.matrix(fpset) # coerces to matrix fpchar <- as.character(fpset) # coerces to character strings as(fpma, "FPset") as(fpchar, "FPset") ## Compound similarity searching with FPset fpSim(x=fpset[1], y=fpset, method="Tanimoto", cutoff=0.4, top=4)showClass("FPset") ## Instance of FPset class data(apset) (fpset <- desc2fp(apset)) view(fpset) ## Class usage fpset[1:4] # behaves like a list fpset[[1]] # returns FP object length(fpset) # number of compounds cid(fpset) # returns compound ids fpset[1] <- 0 # replacement cid(fpset) <- 1:length(fpset) # replaces compound ids c(fpset[1:4], fpset[11:14]) # concatenation ## Coerce FPset from/to other objects fpma <- as.matrix(fpset) # coerces to matrix fpchar <- as.character(fpset) # coerces to character strings as(fpma, "FPset") as(fpchar, "FPset") ## Compound similarity searching with FPset fpSim(x=fpset[1], y=fpset, method="Tanimoto", cutoff=0.4, top=4)
Search function for fingerprints, such as PubChem or atom pair fingerprints. Enables structure similarity comparisons, searching and clustering.
fpSim(x, y, sorted=TRUE, method="Tanimoto", addone=1, cutoff=0, top="all", alpha=1, beta=1, parameters=NULL,scoreType="similarity")fpSim(x, y, sorted=TRUE, method="Tanimoto", addone=1, cutoff=0, top="all", alpha=1, beta=1, parameters=NULL,scoreType="similarity")
x |
Query molecule of class |
y |
Subject molecule(s) of class |
sorted |
return results sorted or unsorted |
method |
Similarity coefficient to return. One can choose here from several
predefined similarity measures: "Tanimoto" (default), "Euclidean", "Tversky" or
"Dice". Alternatively, one can pass on any custom similarity function containing the
arguments a, b, c and d. For instance, one can define "myfct <- function(a, b, c, d)
c/(alpha*a + beta*b + c)" and then pass on The predefined methods will run a C++ version of this function which is about twice as fast as the R version. When a custom similarity function is given however, it will fall back to using the R version. |
addone |
Value to add to numerator and denominator of similarity coefficient to avoid devision by zero when fingerprint(s) contain only "off-bits" (zeros). Note: if |
cutoff |
allows to restrict results to hits above a similarity cutoff value; default |
top |
allows to restrict number of subject molecules to return; default |
alpha |
Only used when method="Tversky". Allows to specify the weighting variable 'alpha' of the Tversky index: c/(alpha*a + beta*b + c) |
beta |
Only used when method="Tversky". Allows to specify the weighting variable 'beta' of the Tversky index. |
parameters |
Parameters for computing Z-scores, E-values, and p-values. Pass this data if you want these
scores returned. This data can be generated with the |
scoreType |
If using the |
Returns numeric vector with similarity coefficients as values and compound identifiers as names.
Thomas Girke, Kevin Horan
Tanimoto similarity coefficient: Tanimoto TT (1957) IBM Internal Report 17th Nov see also Jaccard P (1901) Bulletin del la Societe Vaudoisedes Sciences Naturelles 37, 241-272.
PubChem fingerprint specification: ftp://ftp.ncbi.nih.gov/pubchem/specifications/pubchem_fingerprints.txt
Functions: fp2bit
## Load PubChem SDFset sample data(sdfsample); sdfset <- sdfsample cid(sdfset) <- sdfid(sdfset) ## Convert base 64 encoded fingerprints to character vector or binary matrix fpset <- fp2bit(sdfset) ## Alternatively, one can use atom pair fingerprints ## Not run: fpset <- desc2fp(sdf2ap(sdfset)) ## End(Not run) ## Pairwise compound structure comparisons fpSim(x=fpset[1], y=fpset[2], method="Tanimoto") ## Structure similarity searching: x is query and y is fingerprint database fpSim(x=fpset[1], y=fpset) ## Controlling the output fpSim(x=fpset[1], y=fpset, method="Tversky", cutoff=0.4, top=4, alpha=0.5, beta=1) ## Use custom distance function myfct <- function(a, b, c, d) c/(a+b+c+d) fpSim(x=fpset[1], y=fpset, method=myfct) ## Compute fingerprint-based Tanimoto similarity matrix simMA <- sapply(cid(fpset), function(x) fpSim(x=fpset[x], fpset, sorted=FALSE)) ## Hierarchical clustering with simMA as input hc <- hclust(as.dist(1-simMA), method="single") ## Plot hierarchical clustering tree plot(as.dendrogram(hc), edgePar=list(col=4, lwd=2), horiz=TRUE)## Load PubChem SDFset sample data(sdfsample); sdfset <- sdfsample cid(sdfset) <- sdfid(sdfset) ## Convert base 64 encoded fingerprints to character vector or binary matrix fpset <- fp2bit(sdfset) ## Alternatively, one can use atom pair fingerprints ## Not run: fpset <- desc2fp(sdf2ap(sdfset)) ## End(Not run) ## Pairwise compound structure comparisons fpSim(x=fpset[1], y=fpset[2], method="Tanimoto") ## Structure similarity searching: x is query and y is fingerprint database fpSim(x=fpset[1], y=fpset) ## Controlling the output fpSim(x=fpset[1], y=fpset, method="Tversky", cutoff=0.4, top=4, alpha=0.5, beta=1) ## Use custom distance function myfct <- function(a, b, c, d) c/(a+b+c+d) fpSim(x=fpset[1], y=fpset, method=myfct) ## Compute fingerprint-based Tanimoto similarity matrix simMA <- sapply(cid(fpset), function(x) fpSim(x=fpset[x], fpset, sorted=FALSE)) ## Hierarchical clustering with simMA as input hc <- hclust(as.dist(1-simMA), method="single") ## Plot hierarchical clustering tree plot(as.dendrogram(hc), edgePar=list(col=4, lwd=2), horiz=TRUE)
Returns the type label of this fingerprint
fptype(x)fptype(x)
x |
Either an |
The type label of this fingerprint.
Kevin Horan
fp = new("FP",fp=c(1,0,1,1, 0,0,1,0),type="testFP") type = fptype(fp) # == "testFP"fp = new("FP",fp=c(1,0,1,1, 0,0,1,0),type="testFP") type = fptype(fp) # == "testFP"
Converts a nearest neighbor matrix into a list that can be used with the jarvisPatrick function.
fromNNMatrix(data, names = rownames(data))fromNNMatrix(data, names = rownames(data))
data |
A matrix containing integer valued indexes which represent items to be clustered. The index values contained in the matrix must be smaller than the number of rows in the matrix. Each row in the matrix represents one item and the columns are the nearest neighbors of that item. |
names |
The names for each row. The rownames of data will be used if not given. |
A list containing the slots "indexes" and "names".
Kevin Horan
data(apset) nn = nearestNeighbors(apset,cutoff=0.6) nnMatrix = nn$indexes cl = jarvisPatrick(fromNNMatrix(nnMatrix),k=2)data(apset) nn = nearestNeighbors(apset,cutoff=0.6) nnMatrix = nn$indexes cl = jarvisPatrick(fromNNMatrix(nnMatrix),k=2)
Generates Atom Pair descriptors using a fast C function.
genAPDescriptors(sdf,uniquePairs=TRUE)genAPDescriptors(sdf,uniquePairs=TRUE)
sdf |
A single SDF object. |
uniquePairs |
When the same atom pair occurs more than once in a single compound, should the names be unique or not? Setting this to true will take slightly longer to compute. |
A vector of descriptors for the compound given. An AP object can be generated as shown in the example below.
Kevin Horan
library(ChemmineR) data(sdfsample) sdf = sdfsample[[2]] ap = new("AP", AP=genAPDescriptors(sdf))library(ChemmineR) data(sdfsample) sdf = sdfsample[[2]] ap = new("AP", AP=genAPDescriptors(sdf))
Uses Open Babel to compute 3D coordinates given an SDFset with only 2D coordinates.
generate3DCoords(sdf)generate3DCoords(sdf)
sdf |
Any sdfset object. |
A new SDFset in which all compounds have 3D coordinates.
Kevin Horan
## Not run: data(sdfsample) sdf3D = generate3DCoords(sdfsample[1]) ## End(Not run)## Not run: data(sdfsample) sdf3D = generate3DCoords(sdfsample[1]) ## End(Not run)
Generate statistics from a fingerprint database for use in caluclating z-scores, E-values, and p-values later.
genParameters(fpset, similarity = fpSim, sampleFraction = 1, ...)genParameters(fpset, similarity = fpSim, sampleFraction = 1, ...)
fpset |
The database of fingerprints. Needs to be in the format expected by the similarity function.
For the default similarity function, this would be an |
similarity |
A function to compute the similarity between two fingerprints. The first argument should be a single query and the second argument should be a set of fingerprints. |
sampleFraction |
The fraction of all pairs to use for estimating parameters. See Details section. |
... |
Extra parameters will be passed on to the similarity function. |
A beta function will be fit to the distribution of similarity scores
produced by the given similarity function. By default, all pairwise similarities will be
computed. Since this can be expensive for large databases, one can also sample pairs to use.
This can be done by setting sampleFraction to the fraction of all pairwise similarities to
use. For example, for a database of 100 fingerprints, there are 10,000 pairs. Setting
sampleFraction to 0.5 will result in only 5,000 pairs being used to estimate the
parameters.
Parameters are conditioned on the number of set bits. This function therefore groups fingerprints by the number of set bits they have and then estimates parameters for each group. A set of global parameters is also estimated and returned for use in cases where there was not enough data to estimate the parameters for a particular number of set bits.
A data frame with the following columns:
count |
The number of similarities used to estimate these parameters |
avg |
the mean |
variance |
the variance |
alpha |
The alpha paramber of the Beta function |
beta |
The beta parameter of the Beta function |
There will be a row for each possible count of 1 bits. So for a database of 1024 bit fingerprints, there will be 1025 rows for the possible values of 0-1024 bits. There will also be one additional row at the end with the global parameters. This can be used for cases where there are no parameters estimated for the current query 1-bit count.
Kevin Horan
Pierre Baldi and Ramzi Nasr, "When is Chemical Similarity Significant? The Statistical Distribution of Chemical Similarity Scores and Its Extreme Values" Journal of Chemical Information and Modeling 2010 50 (7), 1205-1222
library(ChemmineR) data(apset) fpset=desc2fp(apset) #get a fingerprint database params = genParameters(fpset) scores = fpSim(fpset[[1]],fpset,parameters=params,top=10)library(ChemmineR) data(apset) fpset=desc2fp(apset) #get a fingerprint database params = genParameters(fpset) scores = fpSim(fpset[[1]],fpset,parameters=params,top=10)
Return a vector of every compound id in the given database.
getAllCompoundIds(conn)getAllCompoundIds(conn)
conn |
A database connection object, such as is returned by |
A vector of compound_id numbers
Kevin Horan
#create and initialize a new SQLite database conn = initDb("test1.db") data(sdfsample) #load data ids=loadSdf(conn,sdfsample) ids2=getAllCompoundIds(conn) #ids == ids2 unlink("test1.db")#create and initialize a new SQLite database conn = initDb("test1.db") data(sdfsample) #load data ids=loadSdf(conn,sdfsample) ids2=getAllCompoundIds(conn) #ids == ids2 unlink("test1.db")
On V3000 formatted compounds, returns the value of the given tag on the given atom number.
getAtomAttr(x,atomId,tag)getAtomAttr(x,atomId,tag)
x |
An |
atomId |
The index of the atom to fetch the tag value from. |
tag |
The name of the tag to fetch the value of on the given atom. |
The value of the given tag on the given atom.
Kevin Horan
## Not run: getAtomAttr(v3Sdfs,10,"CHG") ## End(Not run)## Not run: getAtomAttr(v3Sdfs,10,"CHG") ## End(Not run)
On V3000 formatted compounds, returns the value of the given tag on the given bond number.
getBondAttr(x,bondId,tag)getBondAttr(x,bondId,tag)
x |
An |
bondId |
The index of the bond to fetch the tag value from. |
tag |
The name of the tag to fetch the value of on the given bond. |
The value of the given tag on the given bond.
Kevin Horan
## Not run: getBondAttr(v3Sdfs,10,"CFG") ## End(Not run)## Not run: getBondAttr(v3Sdfs,10,"CFG") ## End(Not run)
Get feature values for specific compounds.
getCompoundFeatures(conn, compoundIds, featureNames, filename = NA, keepOrder = FALSE, allowMissing = FALSE, batchSize = 1e+05)getCompoundFeatures(conn, compoundIds, featureNames, filename = NA, keepOrder = FALSE, allowMissing = FALSE, batchSize = 1e+05)
conn |
A database connection object, such as is returned by |
compoundIds |
A vector of compound_id numbers from this database. These are not compound names. Features will be fetched for each compound given here. |
featureNames |
A vector of features to fetch the value for, for each given compound. |
filename |
If given, dump the results into a comma seperated values (CSV) file instead of returning a data frame. This can avoid some potential memory limits when fetching large sets of data. |
keepOrder |
Ensure that the output order of values matches the order in which the compound ids where given. This will make things a little slower, so should only be used where required. |
allowMissing |
If false, raise an exception if a compound cannot be found, otherwise just silently ignore it and return data for whatever compound were found. |
batchSize |
The number of compounds to fetch in a single query. If you find your running out of memory you can try reducing this
values, as well as try writing the result to a file using the |
If filename is not given, returns a data frame with the compound_id and any given feature names. Each row represents
one compound. If filename is given a filename then no value is returned, but the given file is created.
Kevin Horan
#create and initialize a new SQLite database conn = initDb("test1.db") data(sdfsample) #load data ids=loadSdf(conn,sdfsample, function(sdfset) data.frame(MW = MW(sdfset), rings(sdfset,type="count",upper=6, arom=TRUE)) ) f = getCompoundFeatures(conn,ids,c("mw","rings")) unlink("test1.db")#create and initialize a new SQLite database conn = initDb("test1.db") data(sdfsample) #load data ids=loadSdf(conn,sdfsample, function(sdfset) data.frame(MW = MW(sdfset), rings(sdfset,type="count",upper=6, arom=TRUE)) ) f = getCompoundFeatures(conn,ids,c("mw","rings")) unlink("test1.db")
Fetch the names of the given compound ids, if they exist
getCompoundNames(conn, compoundIds, keepOrder = FALSE, allowMissing = FALSE)getCompoundNames(conn, compoundIds, keepOrder = FALSE, allowMissing = FALSE)
conn |
A database connection object, such as is returned by |
compoundIds |
A vector of compound ids. |
keepOrder |
If true, the order of the output compound ids will be the same as the input names. This imposes a performance hit that can be significant for large datasets, thus it should be left FALSE unless needed. |
allowMissing |
When this is false an error will be raised when compound ids queried were not found in the database. If true, just those that are found will be returned with no error or warning. |
Returns a vector of compound names.The rownames will be the compound ids. Compound ids not found, or for which a name is not defined, will be represented as NA.
Kevin Horan
#create and initialize a new SQLite database conn = initDb("test2.db") data(sdfsample) #just load the data with no features or descriptors ids=loadSdf(conn,sdfsample) getCompoundNames(conn,ids[1:3]) unlink("test3.db")#create and initialize a new SQLite database conn = initDb("test2.db") data(sdfsample) #just load the data with no features or descriptors ids=loadSdf(conn,sdfsample) getCompoundNames(conn,ids[1:3]) unlink("test3.db")
Create SDF objects from the given set of compound ids. Id numbers can be found using the findCompounds function.
getCompounds(conn,compoundIds,filename=NA, keepOrder = FALSE, allowMissing = FALSE)getCompounds(conn,compoundIds,filename=NA, keepOrder = FALSE, allowMissing = FALSE)
conn |
A database connection object, such as is returned by |
compoundIds |
A vector of compound ids, as returned by |
filename |
If given, writes the compounds directly to the file named. |
keepOrder |
If true, the order of the output compound ids will be the same as the input names. This imposes a performance hit that can be significant for large datasets, thus it should be left FALSE unless needed. |
allowMissing |
When this is false an error will be raised when compound ids queried were not found in the database. If true, just those that are found will be returned with no error or warning. |
An SDFset with the requested compounds or nothing if filename was specified.
A warning will be raised if not all compounds could be found.
Kevin Horan
#create and initialize a new SQLite database conn = initDb("test3.db") data(sdfsample) #just load the data with no features or descriptors ids=loadSdf(conn,sdfsample) #returns a SDFset with 3 compounds getCompounds(conn, ids[1:3]) unlink("test3.db")#create and initialize a new SQLite database conn = initDb("test3.db") data(sdfsample) #just load the data with no features or descriptors ids=loadSdf(conn,sdfsample) #returns a SDFset with 3 compounds getCompounds(conn, ids[1:3]) unlink("test3.db")
Accepts one or more PubChem compound ids
and downloads the corresponding compounds from PubChem Power User Gateway (PUG)
returning results in an SDFset container. The ChemMine Tools web service
is used as an intermediate, to translate queries from plain HTTP POST to
a PUG SOAP query.
getIds(cids)getIds(cids)
cids |
A |
SDFset |
for details see ?"SDFset-class" |
Tyler Backman
PubChem PUG SOAP: http://pubchem.ncbi.nlm.nih.gov/pug_soap/pug_soap_help.html
Chemmine web service: http://chemmine.ucr.edu
PubChem help: http://pubchem.ncbi.nlm.nih.gov/search/help_search.html
## Not run: ## fetch 2 compounds from PubChem compounds <- getIds(c(111,123)) ## End(Not run)## Not run: ## fetch 2 compounds from PubChem compounds <- getIds(c(111,123)) ## End(Not run)
SDFset
Convenience grep function for string searching in SDFset containers.
grepSDFset(pattern, x, field = "datablock", mode = "subset", ignore.case = TRUE, ...)grepSDFset(pattern, x, field = "datablock", mode = "subset", ignore.case = TRUE, ...)
pattern |
search pattern |
x |
|
field |
delimits search to specific section in SDF; can be
|
mode |
if |
ignore.case |
|
... |
option to pass on additional arguments |
...
numeric |
index vector where the name field contains the component positions in the |
list |
if |
Thomas Girke
...
Class: SDFset
## Instances of SDFset class data(sdfsample) sdfset <- sdfsample ## String Searching in SDFset q <- grepSDFset("65000", sdfset, field="datablock", mode="subset") as(q, "SDFset") grepSDFset("65000", sdfset, field="datablock", mode="index")## Instances of SDFset class data(sdfsample) sdfset <- sdfsample ## String Searching in SDFset q <- grepSDFset("65000", sdfset, field="datablock", mode="subset") as(q, "SDFset") grepSDFset("65000", sdfset, field="datablock", mode="index")
Returns frequency information of functional groups in molecules provided as SDF or SDFset objects. Alternatively, the function can return for each atom its atom/bond neighbor information.
groups(x, groups = "fctgroup", type = "countMA")groups(x, groups = "fctgroup", type = "countMA")
x |
|
groups |
if |
type |
if |
At this point this function is in an experimental stage.
...
Thomas Girke
...
...
## Instances of SDFset class data(sdfsample) sdfset <- sdfsample ## Enumerate functional groups groups(sdfset[1:20], groups="fctgroup", type="countMA") ## Report atom/bond neighbors groups(sdfset[1:4], groups="neighbors", type="countMA") groups(sdfset[1:4], groups="neighbors", type="count") groups(sdfset[1:4], groups="neighbors", type="all")## Instances of SDFset class data(sdfsample) sdfset <- sdfsample ## Enumerate functional groups groups(sdfset[1:20], groups="fctgroup", type="countMA") ## Report atom/bond neighbors groups(sdfset[1:4], groups="neighbors", type="countMA") groups(sdfset[1:4], groups="neighbors", type="count") groups(sdfset[1:4], groups="neighbors", type="all")
Returns header block(s) from an object of class SDF or SDFset.
header(x)header(x)
x |
object of class |
...
named character vector if SDF is provided or list of named character vectors if SDFset is provided
Thomas Girke
...
atomblock, atomcount, bondblock, datablock, cid, sdfid
## SDF/SDFset instances data(sdfsample) sdfset <- sdfsample sdf <- sdfset[[1]] ## Extract header block header(sdf) header(sdfset[1:4]) ## Replacement methods sdfset[[1]][[1]][1] <- "test" sdfset[[1]] header(sdfset)[1] <- header(sdfset[2]) view(sdfset[1:2])## SDF/SDFset instances data(sdfsample) sdfset <- sdfsample sdf <- sdfset[[1]] ## Extract header block header(sdf) header(sdfset[1:4]) ## Replacement methods sdfset[[1]][[1]][1] <- "test" sdfset[[1]] header(sdfset)[1] <- header(sdfset[2]) view(sdfset[1:2])
This will ensure that the database connection given is ready for use. If it does not find the tables it needs, it will try to create them.
initDb(handle)initDb(handle)
handle |
This can be either a filename, in which case we assume it is the name of an SQLite database and use RSQLite to connect to it, or else any DBI Connection. |
Returns a connection object that can be used with other database oriented functions.
Kevin Horan
RSQLite
#create and initialize a new SQLite database conn = initDb("test.db")#create and initialize a new SQLite database conn = initDb("test.db")
Function to perform Jarvis-Patrick clustering. The algorithm requires a
nearest neighbor table, which consists of neighbors for each item
in the dataset. This information is then used to join items into clusters
with the following requirements:
(a) they are contained in each other's neighbor list
(b) they share at least 'k' nearest neighbors
The nearest neighbor table can be computed with nearestNeighbors.
For standard Jarvis-Patrick clustering, this function takes the number of neighbors to keep for each item.
It also has the option of passing a cutoff similarity value instead of the number of neighbors. In this mode, all
neighbors which meet the cutoff criteria will be included in the table.
This is a setting that is not part of the original Jarvis-Patrick algorithm. It
allows to generate tighter clusters and to minimize some limitations of this method, such as joining completely
unrelated items when clustering small data sets. Other extensions, such as the linkage parameter, can also
help improve the clustering quality.
jarvisPatrick(nnm, k, mode="a1a2b", linkage="single")jarvisPatrick(nnm, k, mode="a1a2b", linkage="single")
nnm |
A nearest neighbor table, as produced by |
k |
Minimum number of nearest neighbors two rows (items) in the nearest neighbor table need to have in common to join them into the same cluster. |
mode |
If |
linkage |
Can be one of "single", "average", or "complete", for single linkage, average linkage and complete linkage merge requirements, respectively. In the context of Jarvis-Patrick, average linkage means that at least half of the pairs between the clusters under consideration must pass the merge requirement. Similarly, for complete linkage, all pairs must pass the merge requirement. Single linkage is the normal case for Jarvis-Patrick and just means that at least one pair must meet the requirement. |
...
Depending on the setting under the type argument, the function returns the clustering result in a
named vector or a nearest neighbor table as matrix.
...
Thomas Girke
Jarvis RA, Patrick EA (1973) Clustering Using a Similarity Measure Based on Shared Near Neighbors. IEEE Transactions on Computers, C22, 1025-1034. URLs: http://davide.eynard.it/teaching/2012_PAMI/JP.pdf, http://www.btluke.com/jpclust.html, http://www.daylight.com/dayhtml/doc/cluster/index.pdf
Functions: cmp.cluster
trimNeighbors
nearestNeighbors
## Load/create sample APset and FPset data(apset) fpset <- desc2fp(apset) ## Standard Jarvis-Patrick clustering on APset/FPset objects jarvisPatrick(nearestNeighbors(apset,numNbrs=6), k=5, mode="a1a2b") jarvisPatrick(nearestNeighbors(fpset,numNbrs=6), k=5, mode="a1a2b") ## Jarvis-Patrick clustering only with requirement (b) jarvisPatrick(nearestNeighbors(fpset,numNbrs=6), k=5, mode="b") ## Modified Jarvis-Patrick clustering with minimum similarity 'cutoff' ## value (here Tanimoto coefficient) jarvisPatrick(nearestNeighbors(fpset,cutoff=0.6, method="Tanimoto"), k=2 ) ## Output nearest neighbor table (matrix) nnm <- nearestNeighbors(fpset,numNbrs=6) ## Perform clustering on precomputed nearest neighbor table jarvisPatrick(nnm, k=5)## Load/create sample APset and FPset data(apset) fpset <- desc2fp(apset) ## Standard Jarvis-Patrick clustering on APset/FPset objects jarvisPatrick(nearestNeighbors(apset,numNbrs=6), k=5, mode="a1a2b") jarvisPatrick(nearestNeighbors(fpset,numNbrs=6), k=5, mode="a1a2b") ## Jarvis-Patrick clustering only with requirement (b) jarvisPatrick(nearestNeighbors(fpset,numNbrs=6), k=5, mode="b") ## Modified Jarvis-Patrick clustering with minimum similarity 'cutoff' ## value (here Tanimoto coefficient) jarvisPatrick(nearestNeighbors(fpset,cutoff=0.6, method="Tanimoto"), k=2 ) ## Output nearest neighbor table (matrix) nnm <- nearestNeighbors(fpset,numNbrs=6) ## Perform clustering on precomputed nearest neighbor table jarvisPatrick(nnm, k=5)
This not meant to be used directly, use jarvisPatrick instead. It is exposed so other
libraries can make use of it.
jarvisPatrick_c(neighbors,minNbrs,fast=TRUE,bothDirections=FALSE,linkage = "single")jarvisPatrick_c(neighbors,minNbrs,fast=TRUE,bothDirections=FALSE,linkage = "single")
neighbors |
A matrix of integers. Non integer matricies will be coerced. Each row represensts one element, indexed 1 to N. The values in row i should be the index value of the neighbors of i. Thus, each value should itself be a valid row index. |
minNbrs |
The minimum number of common neibhbors needed for two elements to be merged. |
fast |
If true, only the neibhors given in each row are checked to see if they share |
bothDirections |
If true, two elements must contain each other in their neighbor list in order to be merged.
If false and fast is true, then only one element must contain the other as a neighbor. If
false and fast is false, than neither element must contain the other as a neighbor, though
in all cases there must still be at least |
linkage |
See |
A cluster array with no names.
Kevin Horan
"jobToken"
Container for storing a reference to a remote job ran on the ChemMine Tools web server.
Objects can be created by calls of the form new("jobToken", ...).
tool_name:Object of class "character"
jobId:Object of class "character"
signature(object = "jobToken"): check the status of a launched job
Tyler William H Backman
See ChemMine Tools at http://chemmine.ucr.edu.
Functions: launchCMTool, toolDetails, listCMTools, result, browseJob, status
showClass("jobToken") ## Not run: ## launch a job on the server and obtain jobToken back job1 <- launchCMTool("pubchemID2SDF", 2244) ## check status of the job status(job1) ## obtain results result1 <- result(job1) result1 ## End(Not run)showClass("jobToken") ## Not run: ## launch a job on the server and obtain jobToken back job1 <- launchCMTool("pubchemID2SDF", 2244) ## check status of the job status(job1) ## obtain results result1 <- result(job1) result1 ## End(Not run)
If a single compound in an SDF file contains more than one disconnected component, this function will return an SDF with only the largest component, removing all other components. This will be applied to each SDF in the given SDFset.
largestComponent(sdfSet)largestComponent(sdfSet)
sdfSet |
any SDFset object. |
a new SDFset containing only single component compounds.
Kevin Horan
## Not run: sdf = smiles2sdf(c("Cl.CCC1C2CC3C4C5(CC(C2C5O)N3C1O)C6=CC=CC=C6N4C TEST")) lg = largestComponent(sdf) ## End(Not run)## Not run: sdf = smiles2sdf(c("Cl.CCC1C2CC3C4C5(CC(C2C5O)N3C1O)C6=CC=CC=C6N4C TEST")) lg = largestComponent(sdf) ## End(Not run)
Accepts a tool name (string), input options, and input data to launch a remote web tool on the ChemMine Tools website.
launchCMTool(tool_name, input = "", ...)launchCMTool(tool_name, input = "", ...)
tool_name |
A tool name matching verbatim an existing tool name as listed by |
input |
Input data in the format required for this tool as listed by |
... |
Additional options as mentioned by running |
By running the function toolDetails on a tool of choice, you can see
a pre-generated example function call for this tool.
jobToken |
for details see ?"jobToken-class" |
Tyler William H Backman
See ChemMine Tools at http://chemmine.ucr.edu.
Functions: toolDetails, listCMTools, result, browseJob, status
## Not run: ## list available tools listCMTools() ## get detailed instructions on using a tool toolDetails("Fingerprint Search") ## download compound 2244 from PubChem job1 <- launchCMTool("pubchemID2SDF", 2244) ## check job status and download result status(job1) result1 <- result(job1) ## End(Not run)## Not run: ## list available tools listCMTools() ## get detailed instructions on using a tool toolDetails("Fingerprint Search") ## download compound 2244 from PubChem job1 <- launchCMTool("pubchemID2SDF", 2244) ## check job status and download result status(job1) result1 <- result(job1) ## End(Not run)
Connects to the ChemMine Tools web service and obtains a list of all available tools, and their input and output formats.
listCMTools()listCMTools()
data.frame |
A four column data.frame which describes a tool on each row |
Tyler William H Backman
See ChemMine Tools at http://chemmine.ucr.edu.
Functions: toolDetails, launchCMTool, result, browseJob, status
## Not run: ## list available tools listCMTools() ## get detailed instructions on using a tool toolDetails("Fingerprint Search") ## download compound 2244 from PubChem job1 <- launchCMTool("pubchemID2SDF", 2244) ## check job status and download result status(job1) result1 <- result(job1) ## End(Not run)## Not run: ## list available tools listCMTools() ## get detailed instructions on using a tool toolDetails("Fingerprint Search") ## download compound 2244 from PubChem job1 <- launchCMTool("pubchemID2SDF", 2244) ## check job status and download result status(job1) result1 <- result(job1) ## End(Not run)
List the available features in the given database. These features can
be used in the findCompounds function.
listFeatures(conn)listFeatures(conn)
conn |
Database connection |
A vector of character feature names.
Kevin Horan
#create and initialize a new SQLite database conn = initDb("test7.db") data(sdfsample) #just load the data with no features or descriptors ids=loadSdf(conn,sdfsample,fct=function(sdfset) cbind(mw=MW(sdfset))) listFeatures(conn) # produces c("mw") unlink("test7.db")#create and initialize a new SQLite database conn = initDb("test7.db") data(sdfsample) #just load the data with no features or descriptors ids=loadSdf(conn,sdfsample,fct=function(sdfset) cbind(mw=MW(sdfset))) listFeatures(conn) # produces c("mw") unlink("test7.db")
Load an SDF or SMILES formatted file or SDFSet objects into the database. This will also load arbitrary features
from the data as well as descriptor data. The fct parameter can be used to specify a function
which will compute features which will then be indexed and stored in the database. These features can later
be used to quickly search for compounds. Descriptors can also be computed and stored in another table.
loadSdf(conn, sdfFile, fct = function(x) data.frame(), descriptors=function(x) data.frame(descriptor=c(),descriptor_type=c()), Nlines = 50000, startline = 1, restartNlines = 1e+05,updateByName=FALSE) loadSmiles(conn, smileFile, ...)loadSdf(conn, sdfFile, fct = function(x) data.frame(), descriptors=function(x) data.frame(descriptor=c(),descriptor_type=c()), Nlines = 50000, startline = 1, restartNlines = 1e+05,updateByName=FALSE) loadSmiles(conn, smileFile, ...)
conn |
A database connection object, such as is returned by |
sdfFile |
Either the filename of an SDF formated file, or and SDFSet object. |
smileFile |
The filename of an SMILES formated file. |
... |
When calling loadSmiles, any of the arguments for loadSdf can be used and will be passed to loadSdf internally. |
fct |
A function to extract features from the data. It will be handed an SDFSet generated from the data being loaded. This may be done in batches, so there is no guarantee that the given SDFSset will contain the whole dataset. This function should return a data frame with a column for each feature and a row for each compound given, and in the same order. Each of these features will become a new, indexed, table in the database which can be used later to search for compounds. The column name will become the feature name. If not given, no features are computed. |
descriptors |
This function will also be given an SDFSet object, which may be done in batches. It should return a data frame with the following two columns: "descriptor" and "descriptor_type". The "descriptor" column should contain a string representation of the descriptor, and "descriptor_type" is the type of the descriptor. Our convention for atom pair is "ap" and "fp" for finger print. The order should be maintained. If not given no descriptors are computed. |
Nlines |
When reading data from a file, the number of lines to read at a time. See also |
startline |
When reading data from a file, the line number to start reading from.See also |
restartNlines |
When reading data from a file and startline > 1, the number of lines to look forward to find the start of the
next compound. See also |
updateByName |
If true we make the assumption that all compounds, both in the existing database and the given dataset, have unique names. This function will then avoid re-adding existing, identical compounds, and will update existing compounds with a new definition if a new compound definition with an existing name is given. If false, we allow duplicate compound names to exist in the database, though not duplicate definitions. So identical compounds will not be re-added, but if a new version of an existing compound is added it will not update the existing one, it will add the modified one as a completely new compound with a new compound id. |
Arguments to loadSmiles are the same as those to loadSdf. LoadSmiles will convert its input into an SDFSet and then call loadSdf.
New features can also be added using this function. However, all compounds must have all features so if new
features are added to a new set of compounds, all existing features must be computable by the fct function
given. If new features are detected, all existing compounds will be run through fct in order to compute
the new features for them as well.
For example, if dataset X is loaded with features F1 and F2, and then at a later time we load dataset Y with
new feature F3, the fct function used to load dataset Y must compute and return features F1, F2, and F3.
loadSdf will call fct with both datasets X and Y so that all features are available for all
compounds. If any features are missing an error will be raised.
If just new features are being added, but no new compounds, use the addNewFeatures function.
Returns the compound id numbers of each compound loaded. These can be used to retrieve compounds later. These are id numbers computed by the database and are not extracted from the compound data itself.
Kevin Horan
#create and initialize a new SQLite database conn = initDb("test6.db") data(sdfsample) #just load the data with no features or descriptors ids=loadSdf(conn,sdfsample) unlink("test6.db") conn = initDb("test5.db") #load data and compute 3 features: molecular weight, with the MW function, # and counts for RINGS and AROMATIC, as computed by rings, which returns a data frame itself. ids=loadSdf(conn,sdfsample, function(sdfset) data.frame(MW = MW(sdfset), rings(sdfset,type="count",upper=6, arom=TRUE)) ) unlink("test5.db")#create and initialize a new SQLite database conn = initDb("test6.db") data(sdfsample) #just load the data with no features or descriptors ids=loadSdf(conn,sdfsample) unlink("test6.db") conn = initDb("test5.db") #load data and compute 3 features: molecular weight, with the MW function, # and counts for RINGS and AROMATIC, as computed by rings, which returns a data frame itself. ids=loadSdf(conn,sdfsample, function(sdfset) data.frame(MW = MW(sdfset), rings(sdfset,type="count",upper=6, arom=TRUE)) ) unlink("test5.db")
Creates unique CMP names by appending a counter to each duplicatation set. The function can be used for any character vector.
makeUnique(x, silent = FALSE)makeUnique(x, silent = FALSE)
x |
|
silent |
|
The function is important to maintain unique compound names in the ID slot of SDFset containers.
character |
of same length as x but without duplications |
Thomas Girke
...
Functions: cid, sdfid
## SDFset instance data(sdfsample) sdfset <- sdfsample ## Create unique compound IDs unique_ids <- makeUnique(sdfid(sdfset)) cid(sdfset) <- unique_ids cid(sdfset[1:4])## SDFset instance data(sdfsample) sdfset <- sdfsample ## Create unique compound IDs unique_ids <- makeUnique(sdfid(sdfset)) cid(sdfset) <- unique_ids cid(sdfset[1:4])
Find a set of compounds that are far away from each other.
maximallyDissimilar(compounds, n, similarity = cmp.similarity)maximallyDissimilar(compounds, n, similarity = cmp.similarity)
compounds |
The set of items from which to pick |
n |
The number of dissimilar items to return. |
similarity |
The similarity function to use. By default Tanimoto will be used on APset objects.
Internally, this will be converted to a distance function using |
This will run in O(length(compounds)n) time. Based on the algorithm described in (Higgs,1997).
A vector of indexes of the dissimilar items.
Kevin Horan
Higgs, R.E., Bemis, K.G., Watson, I.A., and Wikel, J.H. 1997. Experimental designs for selecting molecules from large chemical databases. J. Chem. Inf. Comput. Sci. 37, 861-870
data(apset) maximallyDissimilar(apset,10)data(apset) maximallyDissimilar(apset,10)
Computes the nearest neighbors of descriptors in an FPset or APset object for use with the jarvisPatrick clustering
function. Only one of numNbrs or cutoff should be given, cutoff will take precedence if
both are given. If numNbrs is given, then that many neighbors will be returned for each item in the set.
If cutoff is given, then, for each item X, every neighbor that has a similarity value greater than or equal to
the cutoff will be returned in the neighbor list for X.
nearestNeighbors(x, numNbrs = NULL, cutoff = NULL, ...)nearestNeighbors(x, numNbrs = NULL, cutoff = NULL, ...)
x |
Either an FPset or an APset. |
numNbrs |
Number of neighbors to find for each item. If not enough neighbors can be found the matrix will be padded with NA. |
cutoff |
The minimum similarity value an item must have to another item in order to be included in that
items neighbor list. This parameter takes precedence over |
... |
These parameters will be passed into the distance function used, either |
The return value is a list with the following components:
indexes |
index values of nearest neighbors, for each item. If |
names |
The names of each item in the set, as returned by cid |
similarities |
The similarity values of each neighbor to the item for that row.
This will also be either a list of lists or a matrix, depending on whether or not
|
Kevin Horan
data(sdfsample) ap = sdf2ap(sdfsample) nnm = nearestNeighbors(ap,cutoff=0.5) clustering = jarvisPatrick(nnm,k=2,mode="a1b")data(sdfsample) ap = sdf2ap(sdfsample) nnm = nearestNeighbors(ap,cutoff=0.5) clustering = jarvisPatrick(nnm,k=2,mode="a1b")
Returns the number of bits in a fingerprint.
numBits(x)numBits(x)
x |
Either an |
The number of bits in this fingerprint object.
Kevin Horan
fp = new("FP",fp=c(1,0,1,1, 0,0,1,0)) n = numBits(fp) # == 8fp = new("FP",fp=c(1,0,1,1, 0,0,1,0)) n = numBits(fp) # == 8
Return reference to an OBMol from OpenBabel, if available. Operates on SDF or SDFset objects.
obmol(x)obmol(x)
x |
object of class |
A pointer to an OBMol object, or a vector of pointers for an
SDFset.
Kevin Horan
header, atomcount, bondblock, datablock, cid, sdfid
## SDF/SDFset instances if(ChemmineR:::.haveOB()){ data(sdfsample) sdfset <- sdfsample sdf <- sdfset[[1]] obmolRef = obmol(sdf) }## SDF/SDFset instances if(ChemmineR:::.haveOB()){ data(sdfsample) sdfset <- sdfsample sdf <- sdfset[[1]] obmolRef = obmol(sdf) }
Plots compound structure(s) for molecules stored in SDF and SDFset containers.
openBabelPlot(sdfset, height=600, noHbonds = TRUE, regenCoords=FALSE)openBabelPlot(sdfset, height=600, noHbonds = TRUE, regenCoords=FALSE)
sdfset |
Object of class |
height |
The height of the image in pixels. The generated image is always square, so this will also be the width. |
noHbonds |
If |
regenCoords |
If ChemmineOB is installed and this option is TRUE, then Open
Babel will be used to re-generate the 2D coords for each
compound before plotting it. This often results in a nicer
layout. If you want to save the results of the coord
re-generation, call the |
The function openBablePlot depicts a 2D compound structure based
on the XY-coordinates specified in the atom block of an SDF.
If more than one compound is given in the SDFset, they will be arranged in a grid layout.
Kevin Horan
sdf.visualize
## Not run: ## Import SDFset sample set data(sdfsample) (sdfset <- sdfsample) ## Plot single compound structure openBabelPlot(sdfset[1]) ## Plot several compounds structures openBabelPlot(sdfset[1:4]) ## End(Not run)## Not run: ## Import SDFset sample set data(sdfsample) (sdfset <- sdfsample) ## Plot single compound structure openBabelPlot(sdfset[1]) ## Plot several compounds structures openBabelPlot(sdfset[1:4]) ## End(Not run)
Takes an index set, breaks it into batches and runs the given function on each batch
in parallel using the given cluster. See batchByIndex for the non-parallel version.
When doing a select were the condition is a large number of ids it is not always possible to include them in a single SQL statement. This function will break the list of ids into chunks and allow the indexProcessor to deal with just a small number of ids.
parBatchByIndex(allIndices, indexProcessor, reduce, cl, batchSize = 1e+05)parBatchByIndex(allIndices, indexProcessor, reduce, cl, batchSize = 1e+05)
allIndices |
A vector of values that will be broken into batches and passed as an argument to the
|
indexProcessor |
A function that takes one batch if indices. It is called once for each batch, possibly in
parallel. The return value of this function is collected into a list and passed to the
|
reduce |
This function is run after all jobs have finished. It is called with a list of return values from
the The idea is that this function merges all the results together into one result. |
cl |
A SNOW cluster to run jobs on. |
batchSize |
The size of each batch. The last batch may be smaller than this value. |
The return value of the reduce function is returned.
Kevin Horan
## Not run: cl = makeCluster(2) # create a SNOW cluster #function to run a query for each batch of indexes job = function(indexBatch) dbGetQuery(dbConnection, paste("SELECT weight FROM table WHERE id IN (",paste(indexBatch,collapse=","),")")) # function to combine all the results, in this case by summing them up reduce = function(results) sum(unlist(results)) indices = 1:10000 #run queries in parallel and then sum the results totalWeight = parBatchByIndex(indices,job,reduce,cl, 1000) ## End(Not run)## Not run: cl = makeCluster(2) # create a SNOW cluster #function to run a query for each batch of indexes job = function(indexBatch) dbGetQuery(dbConnection, paste("SELECT weight FROM table WHERE id IN (",paste(indexBatch,collapse=","),")")) # function to combine all the results, in this case by summing them up reduce = function(results) sum(unlist(results)) indices = 1:10000 #run queries in parallel and then sum the results totalWeight = parBatchByIndex(indices,job,reduce,cl, 1000) ## End(Not run)
Plots compound structure(s) for molecules stored in SDF and SDFset containers.
## Convenience plot method # plot(x, griddim, print_cid=cid(x), print=TRUE, ...) ## Less important for user plotStruc(sdf, atomcex = 1.2, atomnum = FALSE, no_print_atoms = c("C"), noHbonds = TRUE, bondspacer = 0.12, colbonds=NULL, bondcol="red", regenCoords=FALSE, ...)## Convenience plot method # plot(x, griddim, print_cid=cid(x), print=TRUE, ...) ## Less important for user plotStruc(sdf, atomcex = 1.2, atomnum = FALSE, no_print_atoms = c("C"), noHbonds = TRUE, bondspacer = 0.12, colbonds=NULL, bondcol="red", regenCoords=FALSE, ...)
sdf |
Object of class |
atomcex |
Font size for atom labels |
atomnum |
If |
no_print_atoms |
Excludes specified atoms from being plotted. |
noHbonds |
If |
bondspacer |
Numeric value specifying the plotting distance for double/triple bonds. |
colbonds |
Highlighting of subgraphs in main structure by providing a numeric vector of atom numbers, here position index in atom block.
The bonds of connected atoms will be plotted in the color provided under |
bondcol |
A character or numeric vector of length one to specify the color to use for substructure highlighting under |
regenCoords |
If ChemmineOB is installed and this option is TRUE, then Open
Babel will be used to re-generate the 2D coords for each
compound before plotting it. This often results in a nicer
layout. If you want to save the results of the coord
re-generation, call the |
... |
Arguments to be passed to/from other methods. |
The function plotStruc depicts a single 2D compound structure based
on the XY-coordinates specified in the atom block of an SDF. The generic method
plot can be used as a convenient shorthand to plot one or many
structures at once. Both functions depend on the availability of the
XY-coordinates in the source SD file and only 2D (not 3D) representations are plotted
correctly.
Additional arguments that can only be passed on to the plot function when supplied with
an SDFset object:
griddim: numeric vector of length two to define the dimensions for arranging several structures in one plot.
print_cid: character vector for printing custom compound labels. Default is print_cid=cid(sdfset).
print: if print=TRUE, then a summary of the SDF content for each supplied compound is printed to the screen.
This behavior is turned off with print=TRUE.
Prints summary of SDF/SDFset to screen and plots their structures to graphics device.
The compound depictions created by this function are not as pretty as the structure representations generated with the sdf.visualize function. This will be improved in the future.
Thomas Girke
...
sdf.visualize
## Import SDFset sample set data(sdfsample) (sdfset <- sdfsample) ## Plot single compound structure plotStruc(sdfset[[1]]) ## Plot several compounds structures plot(sdfset[1:4]) ## Highlighting substructures (here all rings) myrings <- as.numeric(gsub(".*_", "", unique(unlist(rings(sdfset[1]))))) plot(sdfset[1], colbonds=myrings) ## Customize plot plot(sdfset[1:4], griddim=c(2,2), print_cid=letters[1:4], print=FALSE, noHbonds=FALSE)## Import SDFset sample set data(sdfsample) (sdfset <- sdfsample) ## Plot single compound structure plotStruc(sdfset[[1]]) ## Plot several compounds structures plot(sdfset[1:4]) ## Highlighting substructures (here all rings) myrings <- as.numeric(gsub(".*_", "", unique(unlist(rings(sdfset[1]))))) plot(sdfset[1], colbonds=myrings) ## Customize plot plot(sdfset[1:4], griddim=c(2,2), print_cid=letters[1:4], print=FALSE, noHbonds=FALSE)
Generates the following descriptors: "cansmi", "cansmiNS", "formula", "HBA1", "HBA2", "HBD", "InChI", "InChIKey", "logP", "MR", "MW", "nF","title", "TPSA".
propOB(sdfSet)propOB(sdfSet)
sdfSet |
An SDFset object. |
A data frame with a row for each compound in the given data frame and a named column for each property.
Kevin Horan
## Not run: library(ChemmineR) data(sdfsample) propOB(sdfsample) ## End(Not run)## Not run: library(ChemmineR) data(sdfsample) propOB(sdfsample) ## End(Not run)
Accepts one or more PubChem compound ids
and downloads the corresponding compounds from PubChem Power User Gateway (PUG)
returning results in an SDFset container.
pubchemCidToSDF(cids)pubchemCidToSDF(cids)
cids |
A |
SDFset |
for details see ?"SDFset-class" |
Kevin Horan
PubChem PUG REST: https://pubchem.ncbi.nlm.nih.gov/pug_rest/PUG_REST_Tutorial.html
## Not run: ## fetch 2 compounds from PubChem compounds <- pubchemCidToSDF(c(111,123)) ## End(Not run)## Not run: ## fetch 2 compounds from PubChem compounds <- pubchemCidToSDF(c(111,123)) ## End(Not run)
Data frame with bit positions and substructure specifications.
data(pubchemFPencoding)data(pubchemFPencoding)
The format is a data frame with 881 rows and 2 columns.
From: ftp://ftp.ncbi.nih.gov/pubchem/specifications/pubchem_fingerprints.txt
See: ftp://ftp.ncbi.nih.gov/pubchem/specifications/pubchem_fingerprints.txt
data(pubchemFPencoding) pubchemFPencoding[1:4,]data(pubchemFPencoding) pubchemFPencoding[1:4,]
Use PubChem API to get CIDs by InChI sttrings. This function sends one request per InChI. For courtesy, it is not recommended to parellelize this function.
pubchemInchi2cid(inchis, verbose = TRUE)pubchemInchi2cid(inchis, verbose = TRUE)
inchis |
Character vector of InChI strings |
verbose |
Logical, show verbose information? |
a numeric vector of CIDs with names. Successful requests will have empty names, requests with invalid InChI strings will have name "invalid" and requests with valid InChI but not found in PubChem will have name "not_found"
Le Zhang
PubChem PUG REST: https://pubchem.ncbi.nlm.nih.gov/pug_rest/PUG_REST_Tutorial.html
## Not run: inchis <- c( "InChI=1S/C15H26O/c1-9(2)11-6-5-10(3)15-8-7-14(4,16)13(15)12(11)15/h9-13,16H,5-8H2,1-4H3/t10-,11+,12-,13+,14+,15-/m1/s1", "InChI=1S/C3H8/c1-3-2/h3H2,1-2H3", "InChI=1S/C15H20Br2O2/c1-2-12(17)13-7-3-4-8-14-15(19-13)10-11(18-14)6-5-9-16/h3-4,6,9,11-15H,2,7-8,10H2,1H3/t5-,11-,12+,13+,14-,15-/m1/s1", "InChI=abc" ) pubchemInchi2cid(inchis) ## End(Not run)## Not run: inchis <- c( "InChI=1S/C15H26O/c1-9(2)11-6-5-10(3)15-8-7-14(4,16)13(15)12(11)15/h9-13,16H,5-8H2,1-4H3/t10-,11+,12-,13+,14+,15-/m1/s1", "InChI=1S/C3H8/c1-3-2/h3H2,1-2H3", "InChI=1S/C15H20Br2O2/c1-2-12(17)13-7-3-4-8-14-15(19-13)10-11(18-14)6-5-9-16/h3-4,6,9,11-15H,2,7-8,10H2,1H3/t5-,11-,12+,13+,14-,15-/m1/s1", "InChI=abc" ) pubchemInchi2cid(inchis) ## End(Not run)
Use PubChem API to get CIDs by InChIKeys
pubchemInchikey2sdf(inchikeys)pubchemInchikey2sdf(inchikeys)
inchikeys |
Character vector, InChIKey strings. |
a list of 2 items. the first item "sdf_set" is a 'SDFset' object. It contains all queried and successful SDF infomation. The second item "sdf_index" is a named numeric vector. It records whether all input InChIKeys have successful returns in the 'SDFset' object. If so, a non-zero value is returned as the index of where it exists in the 'SDFset' object, if not, 0 is returned.
Le Zhang
PubChem PUG REST: https://pubchem.ncbi.nlm.nih.gov/pug_rest/PUG_REST_Tutorial.html
## Not run: ## fetch 2 compounds from PubChem inchikeys <- c( "ZFUYDSOHVJVQNB-FZERPYLPSA-N", "KONGRWVLXLWGDV-BYGOPZEFSA-N", "AANKDJLVHZQCFG-WLIQWNBFSA-N", "SNFRINMTRPQQLE-JQWAAABSSA-N" ) pubchemInchikey2sdf(inchikeys) ## End(Not run)## Not run: ## fetch 2 compounds from PubChem inchikeys <- c( "ZFUYDSOHVJVQNB-FZERPYLPSA-N", "KONGRWVLXLWGDV-BYGOPZEFSA-N", "AANKDJLVHZQCFG-WLIQWNBFSA-N", "SNFRINMTRPQQLE-JQWAAABSSA-N" ) pubchemInchikey2sdf(inchikeys) ## End(Not run)
Takes any compound name and queries pubchem to find its pubchem id (CID).
pubchemName2CID(name)pubchemName2CID(name)
name |
Any compound name, used to query pubchem to find the compound. |
The result is the pubchem compound id. If the name is not found, NA will be returned.
Kevin Horan
PubChem PUG REST: https://pubchem.ncbi.nlm.nih.gov/pug_rest/PUG_REST_Tutorial.html
## Not run: ## fetch 2 compounds from PubChem cid <- pubchemName2CID("CHEMBL460363") ## End(Not run)## Not run: ## fetch 2 compounds from PubChem cid <- pubchemName2CID("CHEMBL460363") ## End(Not run)
Accepts one SDFset container
and performs a similarity PubChem fingerprint search, returning
hits in an SDFset container. If the input object
contains multiple items, only the first is used as a query.
pubchemSDFSearch(sdf)pubchemSDFSearch(sdf)
sdf |
A |
SDFset |
for details see ?"SDFset-class" |
Kevin Horan
PubChem PUG REST: https://pubchem.ncbi.nlm.nih.gov/pug_rest/PUG_REST_Tutorial.html
SMILES Format: http://en.wikipedia.org/wiki/Chemical_file_format#SMILES
## Not run: ## get a sample compound data(sdfsample); sdfset <- sdfsample[2] ## search a compound on PubChem compounds <- pubchemSDFSearch(sdfset) ## End(Not run)## Not run: ## get a sample compound data(sdfsample); sdfset <- sdfsample[2] ## search a compound on PubChem compounds <- pubchemSDFSearch(sdfset) ## End(Not run)
Accepts one SMILE string or SMIset container
and performs a PubChem fingerprint search, returning
hits in an SDFset container. If the input object
contains multiple items, only the first is used as a query.
pubchemSmilesSearch(smiles)pubchemSmilesSearch(smiles)
smiles |
A |
SDFset |
for details see ?"SDFset-class" |
Kevin Horan
PubChem PUG REST: https://pubchem.ncbi.nlm.nih.gov/pug_rest/PUG_REST_Tutorial.html
SMILES Format: http://en.wikipedia.org/wiki/Chemical_file_format#SMILES
## Not run: ## get a sample compound data(sdfsample); sdfset <- sdfsample[2] ## search a compound on PubChem compounds <- pubchemSmilesSearch(sdfset) ## End(Not run)## Not run: ## get a sample compound data(sdfsample); sdfset <- sdfsample[2] ## search a compound on PubChem compounds <- pubchemSmilesSearch(sdfset) ## End(Not run)
Function to convert atom pairs (AP) or fingerprints (e.g. AP fingerprints) stored as character strings to APset or FPset objects (e.g. generated by sdfStream). Alternatively, one can provide the AP or fingerprint strings in a named character vector.
read.AP(x, type, colid, isFile = class(x) == "character" & length(x) == 1)read.AP(x, type, colid, isFile = class(x) == "character" & length(x) == 1)
x |
name of file from where to read the AP/APFP character strings; or named character vector containing the AP/APFP strings |
type |
|
colid |
column containing AP/FP character strings if |
isFile |
Is |
...
object of class APset or FPset
Thomas Girke
...
sdf2ap, sdfStream
## Load sample data library(ChemmineR) data(sdfsample); sdfset <- sdfsample ## Not run: write.SDF(sdfset, "test.sdf") ## Define descriptor set in a simple function desc <- function(sdfset) { cbind(SDFID=sdfid(sdfset), # datablock2ma(datablocklist=datablock(sdfset)), MW=MW(sdfset), groups(sdfset), APFP=desc2fp(x=sdf2ap(sdfset), descnames=1024, type="character"), AP=sdf2ap(sdfset, type="character"), rings(sdfset, type="count", upper=6, arom=TRUE) ) } ## Run sdfStream with desc function and write results to a file called 'matrix.xls' sdfStream(input="test.sdf", output="matrix.xls", fct=desc, Nlines=1000) ## Select molecules from SD File using line index from sdfStream indexDF <- read.delim("matrix.xls", row.names=1)[,1:4] indexDFsub <- indexDF[indexDF$MW < 400, ] # Selects molecules with MW < 400 sdfset <- read.SDFindex(file="test.sdf", index=indexDFsub, type="SDFset") ## Write result directly to SD file without storing larger numbers of molecules in memory read.SDFindex(file="test.sdf", index=indexDFsub, type="file", outfile="sub.sdf") ## Read AP/APFP strings from file into APset or FP object apset <- read.AP(x="matrix.xls", type="ap", colid="AP") apfp <- read.AP(x="matrix.xls", type="apfp", colid="APFP") ## Alternatively, one can provide the AP/APFP strings in a named character vector apset <- read.AP(x=sdf2ap(sdfset[1:20], type="character"), type="ap") apfp <- read.AP(x=desc2fp(x=sdf2ap(sdfset[1:20]), descnames=1024, type="character"), type="apfp") ## End(Not run)## Load sample data library(ChemmineR) data(sdfsample); sdfset <- sdfsample ## Not run: write.SDF(sdfset, "test.sdf") ## Define descriptor set in a simple function desc <- function(sdfset) { cbind(SDFID=sdfid(sdfset), # datablock2ma(datablocklist=datablock(sdfset)), MW=MW(sdfset), groups(sdfset), APFP=desc2fp(x=sdf2ap(sdfset), descnames=1024, type="character"), AP=sdf2ap(sdfset, type="character"), rings(sdfset, type="count", upper=6, arom=TRUE) ) } ## Run sdfStream with desc function and write results to a file called 'matrix.xls' sdfStream(input="test.sdf", output="matrix.xls", fct=desc, Nlines=1000) ## Select molecules from SD File using line index from sdfStream indexDF <- read.delim("matrix.xls", row.names=1)[,1:4] indexDFsub <- indexDF[indexDF$MW < 400, ] # Selects molecules with MW < 400 sdfset <- read.SDFindex(file="test.sdf", index=indexDFsub, type="SDFset") ## Write result directly to SD file without storing larger numbers of molecules in memory read.SDFindex(file="test.sdf", index=indexDFsub, type="file", outfile="sub.sdf") ## Read AP/APFP strings from file into APset or FP object apset <- read.AP(x="matrix.xls", type="ap", colid="AP") apfp <- read.AP(x="matrix.xls", type="apfp", colid="APFP") ## Alternatively, one can provide the AP/APFP strings in a named character vector apset <- read.AP(x=sdf2ap(sdfset[1:20], type="character"), type="ap") apfp <- read.AP(x=desc2fp(x=sdf2ap(sdfset[1:20]), descnames=1024, type="character"), type="apfp") ## End(Not run)
Extracts specific molecules from SD File based on a line position index computed by the sdfStream function.
read.SDFindex(file, index, type = "SDFset", outfile)read.SDFindex(file, index, type = "SDFset", outfile)
file |
file name of source SD file used to generate |
index |
data frame containing in the first two columns the start and end positions (index) of molecules in an SD File, respectively. Typically, this index would be imported with |
type |
if |
outfile |
name of output file when |
...
Writes molecules in SDF format to file or collects them in SDFset container.
Thomas Girke
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Import/export functions: read.SDFset, read.SDFstr, read.SDFstr, read.SDFset, write.SDFsplit
## Load sample data library(ChemmineR) data(sdfsample); sdfset <- sdfsample ## Not run: write.SDF(sdfset, "test.sdf") ## Define descriptor set in a simple function desc <- function(sdfset) { cbind(SDFID=sdfid(sdfset), # datablock2ma(datablocklist=datablock(sdfset)), MW=MW(sdfset), groups(sdfset), # AP=sdf2ap(sdfset, type="character"), rings(sdfset, type="count", upper=6, arom=TRUE) ) } ## Run sdfStream with desc function and write results to a file called 'matrix.xls' sdfStream(input="test.sdf", output="matrix.xls", fct=desc, Nlines=1000) ## Select molecules from SD File using line index from sdfStream indexDF <- read.delim("matrix.xls", row.names=1)[,1:4] indexDFsub <- indexDF[indexDF$MW < 400, ] # Selects molecules with MW < 400 sdfset <- read.SDFindex(file="test.sdf", index=indexDFsub, type="SDFset") ## Write result directly to SD file without storing larger numbers of molecules in memory read.SDFindex(file="test.sdf", index=indexDFsub, type="file", outfile="sub.sdf") ## End(Not run)## Load sample data library(ChemmineR) data(sdfsample); sdfset <- sdfsample ## Not run: write.SDF(sdfset, "test.sdf") ## Define descriptor set in a simple function desc <- function(sdfset) { cbind(SDFID=sdfid(sdfset), # datablock2ma(datablocklist=datablock(sdfset)), MW=MW(sdfset), groups(sdfset), # AP=sdf2ap(sdfset, type="character"), rings(sdfset, type="count", upper=6, arom=TRUE) ) } ## Run sdfStream with desc function and write results to a file called 'matrix.xls' sdfStream(input="test.sdf", output="matrix.xls", fct=desc, Nlines=1000) ## Select molecules from SD File using line index from sdfStream indexDF <- read.delim("matrix.xls", row.names=1)[,1:4] indexDFsub <- indexDF[indexDF$MW < 400, ] # Selects molecules with MW < 400 sdfset <- read.SDFindex(file="test.sdf", index=indexDFsub, type="SDFset") ## Write result directly to SD file without storing larger numbers of molecules in memory read.SDFindex(file="test.sdf", index=indexDFsub, type="file", outfile="sub.sdf") ## End(Not run)
SDFset
Imports one or many molecules from an SD/MOL file and stores it in an SDFset container.
Supports both the V2000 and V3000 formats.
read.SDFset(sdfstr = sdfstr,skipErrors=FALSE, ...)read.SDFset(sdfstr = sdfstr,skipErrors=FALSE, ...)
sdfstr |
path/name to an SD file; alternatively an |
skipErrors |
If true, molecules which fail to parse will be removed from the output. Otherwise and error will be thrown and processing of the input will stop. |
... |
option to pass on additional arguments. Possible arguments are: datablock: true or false, whether to include the data fields or not. Defaults to TRUE. tail2vec: true or false, whether to return data feilds as a vector or not. Defaults to TRUE. extendedAttributes: true or false, whether to parse the extended attributes available on the V3000 format. Defaults to FALSE. When set to TRUE, the resulting objects will be of type ExtSDF, which is a sub-class of SDF. However, some functions, such as plot, may not work with this type right now. |
...
SDFset |
for details see ?"SDFset-class" |
Thomas Girke
SDF format defintion: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Functions: read.SDFstr
## Write instance of SDFset class to SD file data(sdfsample); sdfset <- sdfsample # write.SDF(sdfset[1:4], file="sub.sdf") ## Import SD file # read.SDFset("sub.sdf") ## Pass on SDFstr object sdfstr <- as(sdfset, "SDFstr") read.SDFset(sdfstr)## Write instance of SDFset class to SD file data(sdfsample); sdfset <- sdfsample # write.SDF(sdfset[1:4], file="sub.sdf") ## Import SD file # read.SDFset("sub.sdf") ## Pass on SDFstr object sdfstr <- as(sdfset, "SDFstr") read.SDFset(sdfstr)
SDFstr
Imports one or many molecules from an SD/MOL file and stores it in an SDFstr container.
read.SDFstr(sdfstr)read.SDFstr(sdfstr)
sdfstr |
path/name to an SD file; alternatively one can pass on a |
...
SDFstr |
for details see ?"SDFstr-class" |
Thomas Girke
SDF format defintion: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Functions: read.SDFset
## Write instance of SDFstr class to SD file data(sdfsample); sdfset <- sdfsample sdfstr <- as(sdfset, "SDFstr") # write.SDF(sdfset[1:4], file="sub.sdf") ## Import SD file # read.SDFstr("sub.sdf") ## Pass on SDFstr object sdfstr <- as(sdfset, "SDFstr") read.SDFset(sdfstr)## Write instance of SDFstr class to SD file data(sdfsample); sdfset <- sdfsample sdfstr <- as(sdfset, "SDFstr") # write.SDF(sdfset[1:4], file="sub.sdf") ## Import SD file # read.SDFstr("sub.sdf") ## Pass on SDFstr object sdfstr <- as(sdfset, "SDFstr") read.SDFset(sdfstr)
SMIset
Imports one or many molecules from a SMILES file and stores content in a SMIset container. The input file is expected to
contain one SMILES string per row with tab-separated compound identifiers at the end of each line. The compound identifiers
are optional.
read.SMIset(file, removespaces = TRUE, ...)read.SMIset(file, removespaces = TRUE, ...)
file |
path/name to a SMILES file |
removespaces |
if set to |
... |
option to pass on additional arguments |
...
SMIset |
for details see ?"SMIset-class" |
Thomas Girke
SMILES (Simplified molecular-input line-entry system) format definition: http://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system
Functions: read.SDFset
## Write instance of SMIset class to SMILES file data(smisample); smiset <- smisample # write.SMI(smiset[1:4], file="sub.smi") ## Import SMILES file # read.SMIset("sub.smi")## Write instance of SMIset class to SMILES file data(smisample); smiset <- smisample # write.SMI(smiset[1:4], file="sub.smi") ## Import SMILES file # read.SMIset("sub.smi")
This uses Open Babel (requires ChemmineOB package) to re-generate the 2D coordinates of compounds. This often results in a nicer layout of the compound when plotting.
regenerateCoords(sdf)regenerateCoords(sdf)
sdf |
A SDF or SDFset object whose coordinates will be re-generated. |
Either an SDF object if given an SDF, or else and SDFset.
Kevin Horan
## Not run: data(sdfsample) prettySdfset = regenerateCoords(sdfsample[1:4]) ## End(Not run)## Not run: data(sdfsample) prettySdfset = regenerateCoords(sdfsample[1:4]) ## End(Not run)
Accepts a jobToken job as returned by the function launchCMTool and returns
the final result. If the job is still running, the function will loop until the job is ready.
result(object)result(object)
object |
A |
Output will be in the format specified for this tool, as listed with the listCMTools function.
Tyler William H Backman
See ChemMine Tools at http://chemmine.ucr.edu.
Functions: toolDetails, listCMTools, launchCMTool, browseJob, status
## Not run: ## list available tools listCMTools() ## get detailed instructions on using a tool toolDetails("Fingerprint Search") ## download compound 2244 from PubChem job1 <- launchCMTool("pubchemID2SDF", 2244) ## check job status and download result status(job1) result1 <- result(job1) ## End(Not run)## Not run: ## list available tools listCMTools() ## get detailed instructions on using a tool toolDetails("Fingerprint Search") ## download compound 2244 from PubChem job1 <- launchCMTool("pubchemID2SDF", 2244) ## check job status and download result status(job1) result1 <- result(job1) ## End(Not run)
Identifies all possible rings in molecules using the exhaustive ring perception algorithm from Hanser et al (1996). In addition, the function can return all smallest possible rings as well as aromaticity information for each ring.
Note that large molecules can cause this function to run for an extremely long
amount of time. Use the upper parameter to limit the ring size if run time
is a problem.
rings(x, upper = Inf, type = "all", arom = FALSE, inner = FALSE)rings(x, upper = Inf, type = "all", arom = FALSE, inner = FALSE)
x |
|
upper |
allows to specify an upper length limit for ring predictions. The default setting |
type |
if |
arom |
if Note that setting |
inner |
if |
...
The settings type="all" and type="arom" return lists, and type="count" returns a matrix.
Thomas Girke
Hanser, Jauffret and Kaufmann (1996) A New Algorithm for Exhaustive Ring Perception in a Molecular Graph. Journal of Chemical Information and Computer Sciences, 36: 1146-1152. URL: http://pubs.acs.org/doi/abs/10.1021/ci960322f
...
## Instances of SDFset class data(sdfsample) sdfset <- sdfsample ## Return all possible rings for a single compound rings(sdfset[1], upper=Inf, type="all", arom=FALSE, inner=FALSE) plot(sdfset[1], print=FALSE, atomnum=TRUE, no_print_atoms="H") ## Return all possible rings for several compounds plus their ## aromaticity information rings(sdfset[1:4], upper=Inf, type="all", arom=TRUE, inner=FALSE) ## Return rings with no more than 6 atoms rings(sdfset[1:4], upper=6, type="all", arom=TRUE, inner=FALSE) ## Return rings with no more than 6 atoms that are also armomatic rings(sdfset[1:4], upper=6, type="arom", arom=TRUE, inner=FALSE) ## Return shortest possible rings (no complex rings) rings(sdfset[1:4], upper=Inf, type="all", arom=TRUE, inner=TRUE) ## Count shortest possible rings rings(sdfset[1:4], upper=Inf, type="count", arom=TRUE, inner=TRUE)## Instances of SDFset class data(sdfsample) sdfset <- sdfsample ## Return all possible rings for a single compound rings(sdfset[1], upper=Inf, type="all", arom=FALSE, inner=FALSE) plot(sdfset[1], print=FALSE, atomnum=TRUE, no_print_atoms="H") ## Return all possible rings for several compounds plus their ## aromaticity information rings(sdfset[1:4], upper=Inf, type="all", arom=TRUE, inner=FALSE) ## Return rings with no more than 6 atoms rings(sdfset[1:4], upper=6, type="all", arom=TRUE, inner=FALSE) ## Return rings with no more than 6 atoms that are also armomatic rings(sdfset[1:4], upper=6, type="arom", arom=TRUE, inner=FALSE) ## Return shortest possible rings (no complex rings) rings(sdfset[1:4], upper=Inf, type="all", arom=TRUE, inner=TRUE) ## Count shortest possible rings rings(sdfset[1:4], upper=Inf, type="count", arom=TRUE, inner=TRUE)
Container for storing every element of a single molecule defined in an SD/MOL
file without information loss in a list-like container. The import occurs via
the SDFstr container class. The header block is stored as named
character vector, the atom/bond blocks as matrices and the data block as named
character vector.
Objects can be created by calls of the form new("SDF", ...).
header:Object of class "character"
atomblock:Object of class "matrix"
bondblock:Object of class "matrix"
datablock:Object of class "character"
obmolRef:Object of class "ExternalReferenceOrNULL"
version:Object of class "character"
signature(x = "SDF"): subsetting of class with bracket operator
signature(x = "SDF"): returns one of the four object components
signature(x = "SDF"): replacement method for the four sub-components
signature(x = "SDF"): replacement method for the four sub-components
signature(x = "SDF"): returns atom block as matrix
signature(x = "SDF"): returns atom frequency
signature(x = "SDF"): returns bond block as matrix
signature(x = "SDF"): returns an OBMol pointer
signature(from = "character", to = "SDF"): as(character, "SDF")
signature(from = "list", to = "SDF"): as(list, "SDF")
signature(from = "SDF", to = "character"): as(sdf, "character")
signature(from = "SDF", to = "list"): as(sdf, "list")
signature(from = "SDF", to = "SDFset"): as(sdf, "SDFset")
signature(from = "SDF", to = "SDFstr"): as(SDF, "SDFstr")
signature(from = "SDFset", to = "SDF"): as(sdfset, "SDF")
signature(x = "SDF"): returns data block as named character vector
signature(x = "SDF"): returns data block as named character vector with subsetting support
signature(x = "SDF"): returns header block as named character vector
signature(x = "SDF"): plots molecule structure for SDF object
signature(x = "SDF"): returns SDF object as list
signature(sdf = "SDF"): returns SDF object as character vector
signature(x = "SDF"): returns molecule ID field from header block
signature(object = "SDF"): prints summary of SDF
Thomas Girke
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Related classes: SDFset, SDFstr, AP, APset
showClass("SDF") ## Instances of SDF class data(sdfsample); sdfset <- sdfsample (sdf <- sdfset[[1]]) # returns first molecule in sdfset as SDF object ## Accessing SDF components header(sdf); atomblock(sdf); bondblock(sdf); datablock(sdf) sdfid(sdf) ## Plot molecule structure of SDF plot(sdf) # plots to R graphics device # sdf.visualize(sdf) # viewing in browsershowClass("SDF") ## Instances of SDF class data(sdfsample); sdfset <- sdfsample (sdf <- sdfset[[1]]) # returns first molecule in sdfset as SDF object ## Accessing SDF components header(sdf); atomblock(sdf); bondblock(sdf); datablock(sdf) sdfid(sdf) ## Plot molecule structure of SDF plot(sdf) # plots to R graphics device # sdf.visualize(sdf) # viewing in browser
'sdf.subset' will take a descriptor database generated by 'cmp.parse' and an array of indices, and return an SDF string consisting of SDFs for compounds corresponding to that list of indices. The returned value is a character string.
sdf.subset(db, cmps)sdf.subset(db, cmps)
db |
The database generated by 'cmp.parse' |
cmps |
An array of indecies that correspond to a set of selected compounds from the database |
'sdf.subset' depends on information embedded in the descriptor database returned by 'cmp.parse'. It also relies on the availability of the original SDF where the database has been generated from. Basically, when 'cmp.parse' parses the original SDF file, it will store the path of that SDF file as well as offset information for SDF segment in that file. Therefore, if the SDF file has been changed or deleted, 'sdf.subset' cannot function properly.
The result SDF will also have names added to compounds if they are not present in the original SDF.
Return a character string whose content is the concatenation of SDFs for the selected compounds.
## Note: this functionality has become obsolete since the introduction of the ## 'SDFset' and 'apset' S4 classes. # load sample database from web # db <- cmp.parse("http://bioweb.ucr.edu/ChemMineV2/static/example_db.sdf") # select SDF for 1st and 2nd compound in that SDF # sdf_segments <- sdf.subset(db, c(1, 2)) # now sdf_segments containt the 2 SDFs for those 2 compounds## Note: this functionality has become obsolete since the introduction of the ## 'SDFset' and 'apset' S4 classes. # load sample database from web # db <- cmp.parse("http://bioweb.ucr.edu/ChemMineV2/static/example_db.sdf") # select SDF for 1st and 2nd compound in that SDF # sdf_segments <- sdf.subset(db, c(1, 2)) # now sdf_segments containt the 2 SDFs for those 2 compounds
'sdf.visualize' will take an SDFset object and send the compounds to the ChemMine Tools
website, for visualization and futher analysis. The results are launched in the users web browser.
sdf.visualize(sdf)sdf.visualize(sdf)
sdf |
A |
Returns the URL of the webpage containing all the SDFs and 2D images corresponding to the selected compounds.
Tyler Backman
ChemMine Tools web service: http://chemmine.ucr.edu
cmp.parse, sdf.subset, plotStruc
## Load sample SD file data(sdfsample) sdfset <- sdfsample ## Not run: ## Plot structures using web service ChemMine Tools sdf.visualize(sdfset[1:4]) ## End(Not run)## Load sample SD file data(sdfsample) sdfset <- sdfsample ## Not run: ## Plot structures using web service ChemMine Tools sdf.visualize(sdfset[1:4]) ## End(Not run)
Creates from a SDFset a searchable atom pair library that is stored in a container of class APset.
sdf2ap(sdfset, type = "AP",uniquePairs=TRUE)sdf2ap(sdfset, type = "AP",uniquePairs=TRUE)
sdfset |
Objects of classes |
type |
if |
uniquePairs |
When the same atom pair occurs more than once in a single compound, should the names be unique or not? Setting this to true will take slightly longer to compute. |
...
APset |
if input is |
AP |
if input is |
Thomas Girke
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", J Chem Inf Comput Sci.
Functions: desc2fp, SDF2apcmp, apset2descdb, cmp.search, cmp.similarity
Related classes: SDF, SDFset, SDFstr, APset.
## Instance of SDFset class data(sdfsample) sdfset <- sdfsample[1:50] sdf <- sdfsample[[1]] ## Compute atom pair library ap <- sdf2ap(sdf) (apset <- sdf2ap(sdfset)) view(apset[1:4]) ## Return main components of APset object cid(apset[1:4]) # compound IDs ap(apset[1:4]) # atom pair descriptors ## Return atom pairs in human readable format db.explain(apset[1]) ## Coerce APset to other objects apset2descdb(apset) # returns old list-style AP database tmp <- as(apset, "list") # returns list as(tmp, "APset") # converst list back to APset ## Compound similarity searching with APset cmp.search(apset, apset[1], type=3, cutoff=0.2) plot(sdfset[names(cmp.search(apset, apset[6], type=2, cutoff=0.4))]) ## Identify compounds with identical AP sets cmp.duplicated(apset, type=2) ## Structure similarity clustering cmp.cluster(db=apset, cutoff = c(0.65, 0.5))[1:20,]## Instance of SDFset class data(sdfsample) sdfset <- sdfsample[1:50] sdf <- sdfsample[[1]] ## Compute atom pair library ap <- sdf2ap(sdf) (apset <- sdf2ap(sdfset)) view(apset[1:4]) ## Return main components of APset object cid(apset[1:4]) # compound IDs ap(apset[1:4]) # atom pair descriptors ## Return atom pairs in human readable format db.explain(apset[1]) ## Coerce APset to other objects apset2descdb(apset) # returns old list-style AP database tmp <- as(apset, "list") # returns list as(tmp, "APset") # converst list back to APset ## Compound similarity searching with APset cmp.search(apset, apset[1], type=3, cutoff=0.2) plot(sdfset[names(cmp.search(apset, apset[6], type=2, cutoff=0.4))]) ## Identify compounds with identical AP sets cmp.duplicated(apset, type=2) ## Structure similarity clustering cmp.cluster(db=apset, cutoff = c(0.65, 0.5))[1:20,]
SDF to list for AP generation
Returns SDF class as list containing the components for generating atom pair descriptors.
SDF2apcmp(SDF)SDF2apcmp(SDF)
SDF |
|
...
list |
with atom and bond components |
Thomas Girke
Chen X and Reynolds CH (2002). "Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients", J Chem Inf Comput Sci.
Functions: sdf2ap, apset2descdb, cmp.search, cmp.similarity
## Instances of SDFset class data(sdfsample) sdf <- sdfsample[[1]] ## Return list cmp <- SDF2apcmp(sdf)## Instances of SDFset class data(sdfsample) sdf <- sdfsample[[1]] ## Return list cmp <- SDF2apcmp(sdf)
SDF to list
Returns objects of class SDF as list.
sdf2list(x)sdf2list(x)
x |
object of class |
...
list |
with following components: |
character |
SDF header block |
matrix |
SDF bond block |
matrix |
SDF atom block |
character |
SDF data block |
Thomas Girke
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Functions: sdfstr2list, sdf2str, SDFset2list, SDFset2SDF
## Instance of SDF class data(sdfsample); sdfset <- sdfsample sdf <- sdfset[[1]] ## Return as list sdf2list(sdf) as(sdf, "list") # similar result## Instance of SDF class data(sdfsample); sdfset <- sdfsample sdf <- sdfset[[1]] ## Return as list sdf2list(sdf) as(sdf, "list") # similar result
SDFset to character
Convert SDFset to SMILES (character)
Accepts compounds in an SDFset container and returns the corresponding
SMILES (Simplified Molecular Input Line Entry Specification) strings as SMIset object.
If ChemineOB is available then OpenBabel for the format conversion.
Otherwise the compound is submitted to the ChemMine Tools web service for conversion
with the Open Babel Open Source Chemistry Toolbox. If the input object
contains multiple items, only the first is converted.
sdf2smiles(sdf)sdf2smiles(sdf)
sdf |
A |
character |
for details see ?"character" |
Tyler Backman, Kevin Horan
Chemmine web service: http://chemmine.ucr.edu
Open Babel: http://openbabel.org
SMILES Format: http://en.wikipedia.org/wiki/Chemical_file_format#SMILES
## Not run: ## get a sample compound data(sdfsample); sdfset <- sdfsample[1] ## convert to smiles (smiles <- sdf2smiles(sdfset)) as.character(smiles) ## End(Not run)## Not run: ## get a sample compound data(sdfsample); sdfset <- sdfsample[1] ## convert to smiles (smiles <- sdf2smiles(sdfset)) as.character(smiles) ## End(Not run)
SDF to SDFstr
Converts SDF to SDFstr. Its main use is to facilitate the export to SD files. It contains optional arguments to generate custom SDF output.
sdf2str(sdf, head, ab, bb, db, cid = NULL, sig = FALSE, ...)sdf2str(sdf, head, ab, bb, db, cid = NULL, sig = FALSE, ...)
sdf |
object of class |
head |
optional |
ab |
optional |
bb |
optional |
db |
optional |
cid |
|
sig |
|
... |
option to pass on additional arguments |
If the export function write.SDF is supplied with an SDFset object, then sdf2str is used internally to customize the export of many molecules to a single SD file using the same optional arguments.
sdfstr |
SDF data of one molecule collapsed to character vector |
Thomas Girke
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Coerce functions: sdfstr2list, sdf2str, SDFset2list, SDFset2SDF
Export function: write.SDF
## Instance of SDF class data(sdfsample); sdfset <- sdfsample sdf <- sdfset[[1]] ## Customize SDF blocks for export to SD file sdf2str(sdf=sdf, sig=TRUE, cid=TRUE) # uses default SDF components sdf2str(sdf=sdf, head=letters[1:4], db=NULL) # uses custom components for header and datablock ## The same arguments can be supplied to the write.SDF function for ## batch export of custom SDFs # write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE, db=NULL)## Instance of SDF class data(sdfsample); sdfset <- sdfsample sdf <- sdfset[[1]] ## Customize SDF blocks for export to SD file sdf2str(sdf=sdf, sig=TRUE, cid=TRUE) # uses default SDF components sdf2str(sdf=sdf, head=letters[1:4], db=NULL) # uses custom components for header and datablock ## The same arguments can be supplied to the write.SDF function for ## batch export of custom SDFs # write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE, db=NULL)
Creates and HTML DataTable showing the compound image along with the fields in the compound data block. Using a browser, this table can be filtered and paged, among other things.
This uses the DT library to create the DataTable.
SDFDataTable(sdfset)SDFDataTable(sdfset)
sdfset |
An |
Returns the result of the datatable function from the DT library.
An HTML file can be created from this value by calling the saveWidget function
on it.
Kevin Horan
DT library: https://rstudio.github.io/DT/ DataTables javascript library: https://datatables.net/
## Not run: #depends on ChemmineOB library(ChemmineR) library(DT) data(sdfsample) # this will open a browser to display the result x=SDFDataTable(sdfsample[1:3]) # if no GUI is available or you want to save the HTML result: saveWidget(x,"output.html") ## End(Not run)## Not run: #depends on ChemmineOB library(ChemmineR) library(DT) data(sdfsample) # this will open a browser to display the result x=SDFDataTable(sdfsample[1:3]) # if no GUI is available or you want to save the HTML result: saveWidget(x,"output.html") ## End(Not run)
Returns the compound identifiers from the header block of SDF or SDFset objects.
sdfid(x, tag = 1)sdfid(x, tag = 1)
x |
object of class |
tag |
values from 1-4 to extract different header block fields; SDF ID is in first one (default) |
...
character vector
Thomas Girke
...
atomblock, atomcount, bondblock, datablock, header, cid
## SDF/SDFset instances data(sdfsample) sdfset <- sdfsample sdf <- sdfset[[1]] ## Extract IDs from header block sdfid(sdf, tag=1) sdfid(sdfset[1:4]) ## Extract compound IDs from ID slot in SDFset container cid(sdfset[1:4]) ## Assigning compound IDs and keeping them unique unique_ids <- makeUnique(sdfid(sdfset)) cid(sdfset) <- unique_ids cid(sdfset[1:4])## SDF/SDFset instances data(sdfsample) sdfset <- sdfsample sdf <- sdfset[[1]] ## Extract IDs from header block sdfid(sdf, tag=1) sdfid(sdfset[1:4]) ## Extract compound IDs from ID slot in SDFset container cid(sdfset[1:4]) ## Assigning compound IDs and keeping them unique unique_ids <- makeUnique(sdfid(sdfset)) cid(sdfset) <- unique_ids cid(sdfset[1:4])
SDFset object
First 100 compounds from PubChem SD file: Compound_00650001_00675000.sdf.gz
data(sdfsample)data(sdfsample)
Object of class sdfset
Object stores 100 molecules from a sample SD file.
ftp://ftp.ncbi.nih.gov/pubchem/Compound/CURRENT-Full/SDF/Compound_00650001_00675000.sdf.gz
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
data(sdfsample) sdfset <- sdfsample view(sdfset[1:4])data(sdfsample) sdfset <- sdfsample view(sdfset[1:4])
List-like container for storing one or many objects of class SDF each containing the structure definition information of molecules provided by an SD/MOL file. The SDFset is the most important class in the ChemmmineR package for accessing and manipulating information stored in SD files.
Objects can be created by calls of the form new("SDFset", ...).
SDF:Object of class "list" storing SDF components
ID:Object of class "character" storing compound identifiers
signature(x = "SDFset"): subsetting of class with bracket operator
signature(x = "SDFset"): returns single component as SDF object
signature(x = "SDFset"): replacement method for single SDF component
signature(x = "SDFset"): replacement method for several SDF components
signature(x = "SDFset"): returns all atom blocks as list
signature(x = "SDFset"): returns all atom frequencies as list
signature(x = "SDFset"): returns all bond blocks as list
signature(x = "SDFset"): returns pointers to OBMol objects as a vector
signature(x = "SDFset"): concatenates two SDFset containers
signature(x = "SDFset"): returns all compound identifiers from ID slot
signature(x = "SDFset"): replacement method for header block
signature(x = "SDFset"): replacement method for atom block
signature(x = "SDFset"): replacement method for bond block
signature(x = "SDFset"): replacement method for data block
signature(from = "list", to = "SDFset"): as(list, "SDFset")
signature(from = "SDF", to = "SDFset"): as(sdf, "SDFset")
signature(from = "SDFset", to = "list"): as(sdfset, "list")
signature(from = "SDFset", to = "SDF"): as(sdfset, "SDF")
signature(from = "SDFset", to = "SDFstr"): as(sdfset, "SDFstr")
signature(from = "SDFstr", to = "SDFset"): as(sdfstr, "SDFset")
signature(x = "SDFset"): returns all data blocks as list
signature(x = "SDFset"): returns all data blocks as named as list with subsetting support
signature(x = "SDFset"): returns all header blocks as list
signature(x = "SDFset"): returns number of entries stored in object
signature(x = "SDFset"): plots one or many molecule structures from SDFset object
signature(x = "SDFset"): returns molecule ID field from header block
signature(x = "SDFset"): returns SDFset object as list
signature(x = "SDFset"): returns SDFset object as list with SDF components
signature(x = "SDFset"): replacement method for SDFset component in SDFset using accessor method
signature(object = "SDFset"): prints summary of SDFset
signature(x = "SDFset"): prints extended summary of SDFset
SDFset(SDF, ID): interface to SDFset constructor
Thomas Girke
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Related classes: SDF, SDFstr, AP, APset
Import function: read.SDFset("some_SDF_file")
Export function: write.SDF(sdfset, "some_file.sdf")
showClass("SDFset") ## Instances of SDFset class data(sdfsample); sdfset <- sdfsample sdfset; view(sdfset[1:4]) sdfset[[1]] ## Import and store SD File in SDFset container # sdfset <- read.SDFset("some_SDF_file") ## Miscellaneous accessor methods header(sdfset[1:4]) atomblock(sdfset[1:4]) atomcount(sdfset[1:4]) bondblock(sdfset[1:4]) datablock(sdfset[1:4]) ## Assigning compound IDs and keeping them unique cid(sdfset); sdfid(sdfset) unique_ids <- makeUnique(sdfid(sdfset)) cid(sdfset) <- unique_ids ## Convert data block to matrix blockmatrix <- datablock2ma(datablocklist=datablock(sdfset)) # Converts data block to matrix numchar <- splitNumChar(blockmatrix=blockmatrix) # Splits to numeric and character matrix numchar[[1]][1:4,]; numchar[[2]][1:4,] ## Compute atom frequency matrix, molecular weight and formula propma <- data.frame(MF=MF(sdfset), MW=MW(sdfset), atomcountMA(sdfset)) propma[1:4, ] ## Assign matrix data to data block datablock(sdfset) <- propma view(sdfset[1:4]) ## String Searching in SDFset grepSDFset("650001", sdfset, field="datablock", mode="subset") # To return index, set mode="index") ## Export SDFset to SD file # write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE) ## Plot molecule structure of SDF plot(sdfset[1:4]) # plots to R graphics device # sdf.visualize(sdfset[1:4]) # viewing in browsershowClass("SDFset") ## Instances of SDFset class data(sdfsample); sdfset <- sdfsample sdfset; view(sdfset[1:4]) sdfset[[1]] ## Import and store SD File in SDFset container # sdfset <- read.SDFset("some_SDF_file") ## Miscellaneous accessor methods header(sdfset[1:4]) atomblock(sdfset[1:4]) atomcount(sdfset[1:4]) bondblock(sdfset[1:4]) datablock(sdfset[1:4]) ## Assigning compound IDs and keeping them unique cid(sdfset); sdfid(sdfset) unique_ids <- makeUnique(sdfid(sdfset)) cid(sdfset) <- unique_ids ## Convert data block to matrix blockmatrix <- datablock2ma(datablocklist=datablock(sdfset)) # Converts data block to matrix numchar <- splitNumChar(blockmatrix=blockmatrix) # Splits to numeric and character matrix numchar[[1]][1:4,]; numchar[[2]][1:4,] ## Compute atom frequency matrix, molecular weight and formula propma <- data.frame(MF=MF(sdfset), MW=MW(sdfset), atomcountMA(sdfset)) propma[1:4, ] ## Assign matrix data to data block datablock(sdfset) <- propma view(sdfset[1:4]) ## String Searching in SDFset grepSDFset("650001", sdfset, field="datablock", mode="subset") # To return index, set mode="index") ## Export SDFset to SD file # write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE) ## Plot molecule structure of SDF plot(sdfset[1:4]) # plots to R graphics device # sdf.visualize(sdfset[1:4]) # viewing in browser
SDFset to list
Returns object of class SDFset as list where each component conists of a list of the four SDF sub-components: header block, atom block, bond block and data block.
SDFset2list(x)SDFset2list(x)
x |
object of class |
...
list |
containing one or many lists each with following components: |
character |
SDF header block |
matrix |
SDF bond block |
matrix |
SDF atom block |
character |
SDF data block |
Thomas Girke
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Functions: sdfstr2list, sdf2str, sdf2list, SDFset2SDF
## Instance of SDFset class data(sdfsample); sdfset <- sdfsample sdfset ## Returns sdfset as list SDFset2list(sdfset[1:4]) as(sdfset, "list")[1:4] # similar result## Instance of SDFset class data(sdfsample); sdfset <- sdfsample sdfset ## Returns sdfset as list SDFset2list(sdfset[1:4]) as(sdfset, "list")[1:4] # similar result
SDFset to list with many SDF
Returns object of class SDFset as list were each component consists of an SDF object.
SDFset2SDF(x)SDFset2SDF(x)
x |
object of class |
...
list |
containing one or many |
Thomas Girke
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Functions: sdfstr2list, sdf2str, sdf2list, SDFset2list
## Instance of SDFset class data(sdfsample); sdfset <- sdfsample sdfset ## Returns sdfset as list SDFset2SDF(sdfset[1:4]) as(sdfset, "SDF")[1:4] # similar result view(sdfset[1:4]) # same result## Instance of SDFset class data(sdfsample); sdfset <- sdfsample sdfset ## Returns sdfset as list SDFset2SDF(sdfset[1:4]) as(sdfset, "SDF")[1:4] # similar result view(sdfset[1:4]) # same result
List-like container for storing one or many molecules from an SD (or MOL) file.
Each component of an SDFstr object stores the SD data line by line from
a single molecule in a character vector. The SDFstr class is an
intermediate container to import SD files into the more important SDFset
object or to export the data back from an SDFset container to a valid SD
file.
Objects can be created by calls of the form new("SDFstr", ...).
a:Object of class "list" with character components
signature(x = "SDFstr"): subsetting of class with bracket operator
signature(x = "SDFstr"): returns single component as character vector
signature(x = "SDFstr"): replacement method for single SDFstr component
signature(x = "SDFstr"): replacement method for several SDFstr components
signature(from = "character", to = "SDFstr"): as(character, "SDFstr")
signature(from = "list", to = "SDFstr"): as(list, "SDFstr")
signature(from = "SDF", to = "SDFstr"): as(sdf, "SDFstr")
signature(from = "SDFset", to = "SDFstr"): as(sdfset, "SDFstr")
signature(from = "SDFstr", to = "list"): as(sdfstr, "list")
signature(from = "SDFstr", to = "SDFset"): as(sdfstr, "SDFset")
signature(x = "SDFstr"): returns length of SDFstr
signature(x = "SDFstr"): accessor method to return SDFstr as list
signature(x = "SDFstr"): replacement method for several SDFstr components
signature(object = "SDFstr"): prints summary of SDFstr
Thomas Girke
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Related classes: SDFset, AP, APset
Import function: read.SDFstr("some_SDF_file")
showClass("SDFstr") ## Instances of SDFstr class data(sdfsample); sdfset <- sdfsample sdfstr <- as(sdfset, "SDFstr") sdfstr[1:4] # print summary of container content sdfstr[[1]] # returns character vector ## Import: sdfstr <- read.SDFstr("some_SDF_file") ## Export: write.SDF(sdfstr, "some_file.sdf")showClass("SDFstr") ## Instances of SDFstr class data(sdfsample); sdfset <- sdfsample sdfstr <- as(sdfset, "SDFstr") sdfstr[1:4] # print summary of container content sdfstr[[1]] # returns character vector ## Import: sdfstr <- read.SDFstr("some_SDF_file") ## Export: write.SDF(sdfstr, "some_file.sdf")
SDFstr to list
Returns objects of class SDFstr as list.
sdfstr2list(x)sdfstr2list(x)
x |
object of class |
...
list |
with many of the following components: |
character |
SDF content of one molecule vectorized line by line |
Thomas Girke
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Functions: sdf2list, sdf2str, SDFset2list, SDFset2SDF
## Instance of SDFstr class data(sdfsample); sdfset <- sdfsample sdfstr <- as(sdfset, "SDFstr") ## Return as list sdfstr2list(sdfstr) as(sdfstr, "list") # similar result## Instance of SDFstr class data(sdfsample); sdfset <- sdfsample sdfstr <- as(sdfset, "SDFstr") ## Return as list sdfstr2list(sdfstr) as(sdfstr, "list") # similar result
Streaming function to compute descriptors for large SD Files without consuming much memory. In addition to descriptor values, it returns a line index that defines the positions of each molecule in the source SD File. This line index can be used by the read.SDFindex function to retrieve specific compounds of interest from large SD Files without reading the entire file into memory.
sdfStream(input, output, append=FALSE, fct, Nlines = 10000, startline=1, restartNlines=10000, silent = FALSE, ...)sdfStream(input, output, append=FALSE, fct, Nlines = 10000, startline=1, restartNlines=10000, silent = FALSE, ...)
input |
file name of input SD file |
output |
file name of tabular descriptor file |
append |
if |
fct |
Function to select descriptor sets; any combination of descriptors, supported by |
Nlines |
Number of lines to read from input SD File at a time; the memory consumption will be proportional to this value. |
startline |
For restarting sdfStream at specific line assigned to |
restartNlines |
Number of lines to parse when |
silent |
if |
... |
Arguments to be passed to/from other methods. |
...
Writes a descriptor matrix to a tabular file. The first and last line number (position index) of each molecule is specified in the first two columns of the tabular output file, respectively.
Thomas Girke
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Import/export functions: read.AP, read.SDFset, read.SDFstr, read.SDFstr, read.SDFset, write.SDFsplit
## Load sample data library(ChemmineR) data(sdfsample); sdfset <- sdfsample ## Not run: write.SDF(sdfset, "test.sdf") ## Define descriptor set in a simple function desc <- function(sdfset) { cbind(SDFID=sdfid(sdfset), # datablock2ma(datablocklist=datablock(sdfset)), MW=MW(sdfset), groups(sdfset), # AP=sdf2ap(sdfset, type="character"), rings(sdfset, type="count", upper=6, arom=TRUE) ) } ## Run sdfStream with desc function and write results to a file called 'matrix.xls' sdfStream(input="test.sdf", output="matrix.xls", append=FALSE, fct=desc, Nlines=1000) ## Same as before but starting in SD file at line number 950 sdfStream(input="test.sdf", output="matrix.xls", append=FALSE, fct=desc, Nlines=1000, startline=950) ## Select molecules from SD File using line index from sdfStream indexDF <- read.delim("matrix.xls", row.names=1)[,1:4] indexDFsub <- indexDF[indexDF$MW < 400, ] # Selects molecules with MW < 400 sdfset <- read.SDFindex(file="test.sdf", index=indexDFsub, type="SDFset") ## Write result directly to SD file without storing larger numbers of molecules in memory read.SDFindex(file="test.sdf", index=indexDFsub, type="file", outfile="sub.sdf") ## Read atom pair string representation from file into APset apset <- read.AP(file="matrix.xls", colid="AP") cid(apsdf) <- as.character(indexDF$SDFID) ## End(Not run)## Load sample data library(ChemmineR) data(sdfsample); sdfset <- sdfsample ## Not run: write.SDF(sdfset, "test.sdf") ## Define descriptor set in a simple function desc <- function(sdfset) { cbind(SDFID=sdfid(sdfset), # datablock2ma(datablocklist=datablock(sdfset)), MW=MW(sdfset), groups(sdfset), # AP=sdf2ap(sdfset, type="character"), rings(sdfset, type="count", upper=6, arom=TRUE) ) } ## Run sdfStream with desc function and write results to a file called 'matrix.xls' sdfStream(input="test.sdf", output="matrix.xls", append=FALSE, fct=desc, Nlines=1000) ## Same as before but starting in SD file at line number 950 sdfStream(input="test.sdf", output="matrix.xls", append=FALSE, fct=desc, Nlines=1000, startline=950) ## Select molecules from SD File using line index from sdfStream indexDF <- read.delim("matrix.xls", row.names=1)[,1:4] indexDFsub <- indexDF[indexDF$MW < 400, ] # Selects molecules with MW < 400 sdfset <- read.SDFindex(file="test.sdf", index=indexDFsub, type="SDFset") ## Write result directly to SD file without storing larger numbers of molecules in memory read.SDFindex(file="test.sdf", index=indexDFsub, type="file", outfile="sub.sdf") ## Read atom pair string representation from file into APset apset <- read.AP(file="matrix.xls", colid="AP") cid(apsdf) <- as.character(indexDF$SDFID) ## End(Not run)
Accepts one SDFset container
and performs a >0.9 similarity PubChem fingerprint search, returning up to 200
hits in an SDFset container. The ChemMine Tools web service
is used as an intermediate, to translate queries from plain HTTP POST to
a PubChem Power User Gateway (PUG) query. If the input object
contains multiple items, only the first is used as a query.
searchSim(sdf)searchSim(sdf)
sdf |
A |
SDFset |
for details see ?"SDFset-class" |
Tyler Backman
PubChem PUG SOAP: http://pubchem.ncbi.nlm.nih.gov/pug_soap/pug_soap_help.html
Chemmine web service: http://chemmine.ucr.edu
PubChem help: http://pubchem.ncbi.nlm.nih.gov/search/help_search.html
SMILES Format: http://en.wikipedia.org/wiki/Chemical_file_format#SMILES
## Not run: ## get a sample compound data(sdfsample); sdfset <- sdfsample[2] ## search a compound on PubChem compounds <- searchSim(sdfset) ## End(Not run)## Not run: ## get a sample compound data(sdfsample); sdfset <- sdfsample[2] ## search a compound on PubChem compounds <- searchSim(sdfset) ## End(Not run)
Accepts one SMILES string (Simplified Molecular Input Line Entry Specification)
and performs a >0.95 similarity PubChem fingerprint search, returning the
hits in an SDFset container. The ChemMine Tools web service
is used as an intermediate, to translate queries from plain HTTP POST to
a PubChem Power User Gateway (PUG) query.
searchString(smiles)searchString(smiles)
smiles |
A |
SDFset |
for details see ?"SDFset-class" |
Tyler Backman
PubChem PUG SOAP: http://pubchem.ncbi.nlm.nih.gov/pug_soap/pug_soap_help.html
Chemmine web service: http://chemmine.ucr.edu
PubChem help: http://pubchem.ncbi.nlm.nih.gov/search/help_search.html
SMILES Format: http://en.wikipedia.org/wiki/Chemical_file_format#SMILES
## Not run: ## search a compound on PubChem compounds <- searchString("CC(=O)OC1=CC=CC=C1C(=O)O") ## End(Not run)## Not run: ## search a compound on PubChem compounds <- searchString("CC(=O)OC1=CC=CC=C1C(=O)O") ## End(Not run)
When doing a select were the condition is a large number of ids it is not always possible to include them in a single SQL statement. This function will break the list of ids into chunks and send the query for each batch. The resutls are appended and returned as one data frame.
selectInBatches(conn, allIndices, genQuery, batchSize = 1e+05)selectInBatches(conn, allIndices, genQuery, batchSize = 1e+05)
conn |
Database connection object |
allIndices |
A vector of indices to pass to the genQuery function in batches. |
genQuery |
A function which takes a vector of indices and constructs an SQL SELECT statement returning records for the given indicies. |
batchSize |
How many indicies to put in each batch. |
A data frame with the results of the query as if all inidices had been included in a single SELEcT statement.
Kevin Horan
##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (conn, allIndices, genQuery, batchSize = 1e+05) { batchByIndex(allIndices, function(indexBatch) { df = dbGetQuery(conn, genQuery(indexBatch)) result = rbind(result, df) }, batchSize) result }##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (conn, allIndices, genQuery, batchSize = 1e+05) { batchByIndex(allIndices, function(indexBatch) { df = dbGetQuery(conn, genQuery(indexBatch)) result = rbind(result, df) }, batchSize) result }
This function should be run after loading a complete set of data.
It will find each group of compounds which share the same
descriptor and call the given function, priorityFn,
with the compound_id numbers of the group. This function should
then assign priorities to each compound-descriptor pair, however
it wishes. Priorities are integer values with lower values being
used in preference of higher values.
It is important that this function be called after all data is loaded. It may be that a compound loaded at the beginning of a data set shares a descriptor with a compound loaded near the end of the data set. If the priorities were set at some point in between these then it would not see all the compounds for that one descriptor.
If a SNOW cluster and connection source function are given, it will run in parallel.
Some pre-defined functions that can be use for priorityFn
are:
randomPriorities: Set the priorities of compounds within a descriptor group
randomly.
forestSizePriorities: Set the priority based on the number
of disconnected components (trees) within the compound. Compounds
with fewer trees will have a higher priority (lower numerical
value) than compounds with more trees.
setPriorities(conn,priorityFn,descriptorIds=c(),cl=NULL,connSource=NULL) forestSizePriorities(conn,compIds) randomPriorities(conn,compIds)setPriorities(conn,priorityFn,descriptorIds=c(),cl=NULL,connSource=NULL) forestSizePriorities(conn,compIds) randomPriorities(conn,compIds)
conn |
A database connection object. |
priorityFn |
This function will be called with the compound_id numbers associated with the same descriptor. It should use the id numbers to lookup whatever data it wants to assign a priority to each compound. These priority values will be used to pick a compound to represent the group in cases where only one compound is needed for each descriptor. The function should return a data.frame with the fields "compound_id" and "priority". The order of the rows is not important. |
descriptorIds |
If given then only re-compute priorities for groups involving descriptors in this list. This is useful for updating priorities after adding new compounds to an existing database. |
cl |
A SNOW cluster on which to run jobs on. |
connSource |
A function to create a new database connection with. This will be run once for each new job created. It must return a newly created connection, not a reference to an existing connection. |
compIds |
The compound_id values for each group. |
For setPriorities, no value is returned.
randomPriorities and forestSizePriorities return
a data.frame with columns "compound_id" and "priority".
Kevin Horan
## Not run: data(sdfsample) conn = initDb("sample.db") sdfLoad(conn,sdfsample) setPriorities(conn,forestSizePriorities) ## End(Not run)## Not run: data(sdfsample) conn = initDb("sample.db") sdfLoad(conn,sdfsample) setPriorities(conn,forestSizePriorities) ## End(Not run)
Perform searches for SMARTS patterns using Open Babel (requires ChemmineOB package to be installed).
smartsSearchOB(sdfset, smartsPattern, uniqueMatches = TRUE)smartsSearchOB(sdfset, smartsPattern, uniqueMatches = TRUE)
sdfset |
An SDFset of the compounds you want to search |
smartsPattern |
The SMARTS pattern as a string. |
uniqueMatches |
If true, only return the number of distinct matches, otherwise return the number of all matches. |
Returns a vector of counts, one for each input compound.
Kevin Horan
## Not run: library(ChemmineOB) data(sdfsample) #look for rotable bonds rotableBonds = smartsSearchOB(sdfsample[1:5],"[!$(*#*)&!D1]-!@[!$(*#*)&!D1]",uniqueMatches=FALSE) ## End(Not run)## Not run: library(ChemmineOB) data(sdfsample) #look for rotable bonds rotableBonds = smartsSearchOB(sdfsample[1:5],"[!$(*#*)&!D1]-!@[!$(*#*)&!D1]",uniqueMatches=FALSE) ## End(Not run)
"SMI"
Container for storing the SMILES string of a single molecule.
Objects can be created by calls of the form new("SMI", ...).
smiles:Object of class "character" of length one
signature(x = "SMI"): returns content as character vector
signature(from = "character", to = "SMI"): as(smi, "SMI")
signature(from = "SMIset", to = "SMI"): as(smiset, "SMI")
signature(object = "SMI"): prints summary of SMI
Thomas Girke
SMILES (Simplified molecular-input line-entry system) format definition: http://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system
Related classes: SMIset, SDF, SDFset
showClass("SMI") ## Instances of SMI class data(smisample); smiset <- smisample (smi <- smiset[[1]]) # returns first molecule in smiset as SMI objectshowClass("SMI") ## Instances of SMI class data(smisample); smiset <- smisample (smi <- smiset[[1]]) # returns first molecule in smiset as SMI object
character) to SDFset
Accepts a named vector or SMIset of
SMILES (Simplified Molecular Input Line Entry Specification) strings and
returns its equivalent as an SDFset container.
This function runs in two modes. If ChemmineOB is available then it will use OpenBabel to convert all the given smiles into an SDFset with 2D coordinates. Otherwise the compound is submitted to the ChemMine Tools web service for conversion with the Open Babel Open Source Chemistry Toolbox. In this case only the first element will be used since this is a very slow operation.
smiles2sdf(smiles)smiles2sdf(smiles)
smiles |
A named vector of SMILES strings. The names will be used to name the SDF objects. |
SDFset |
for details see ?"SDFset-class" |
Tyler Backman, Kevin Horan
Chemmine web service: http://chemmine.ucr.edu
Open Babel: http://openbabel.org
SMILES Format: http://en.wikipedia.org/wiki/Chemical_file_format#SMILES
## Not run: ## convert to sdf data(smisample) (sdf <- smiles2sdf(smisample[1:4])) ## End(Not run)## Not run: ## convert to sdf data(smisample) (sdf <- smiles2sdf(smisample[1:4])) ## End(Not run)
SMIset object
First 100 compounds from PubChem SD file (Compound_00650001_00675000.sdf.gz) converted to SMILES format
data(smisample)data(smisample)
Object of class smiset
Object stores 100 molecules from a sample SMILES file.
ftp://ftp.ncbi.nih.gov/pubchem/Compound/CURRENT-Full/SDF/Compound_00650001_00675000.sdf.gz
SMILES (Simplified molecular-input line-entry system) format definition: http://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system
data(smisample) smiset <- smisample view(smiset[1:4])data(smisample) smiset <- smisample view(smiset[1:4])
"SMIset"
List-like container for storing SMILES strings of many compounds.
Objects can be created by calls of the form new("SMIset", ...).
smilist:Object of class "list" with compound identifiers stored in name slots
signature(x = "SMIset"): subsetting of class with bracket operator
signature(x = "SMIset"): returns single component as SMI object
signature(x = "SMIset"): replacement method for one or many entries
signature(x = "SMIset"): returns content as named character vector
signature(x = "SMIset"): concatenates two SMIset containers
signature(x = "SMIset"): returns compound identifiers
signature(x = "SMIset"): replacement method for compound identifiers
signature(from = "character", to = "SMIset"): as(character, "SMIset")
signature(from = "list", to = "SMIset"): as(list, "SMIset")
signature(from = "SMIset", to = "SMI"): as(smiset, "SMI")
signature(x = "SMIset"): returns number of entries stored in object
signature(object = "SMIset"): prints summary of SMIset
signature(x = "SMIset"): prints extended summary of SMIset
Thomas Girke
SMILES (Simplified molecular-input line-entry system) format definition: http://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system
Related classes: SMI, SDF, SDFset
Import function: read.SMIset("some_SMILES_file")
Export function: write.SMI(smiset, "some_file.smi")
showClass("SMIset") ## Instances of SMIset class data(smisample); smiset <- smisample smiset; view(smiset[1:4]) smiset[[1]] ## Import and store SMILES file in SMIset container # smiset <- read.SMIset("some_SMILES_file") ## Miscellaneous accessor methods cid(smiset[1:4]) (smivec <- as.character(smiset[1:4])) ## Construct SMIset from named vector as(smivec, "SMIset") ## Assigning compound IDs and keeping them unique unique_ids <- makeUnique(cid(smiset)) cid(smiset) <- unique_ids ## Export SMIset to SMILES file # write.SMI(smiset[1:4], file="sub.smi", cid=TRUE)showClass("SMIset") ## Instances of SMIset class data(smisample); smiset <- smisample smiset; view(smiset[1:4]) smiset[[1]] ## Import and store SMILES file in SMIset container # smiset <- read.SMIset("some_SMILES_file") ## Miscellaneous accessor methods cid(smiset[1:4]) (smivec <- as.character(smiset[1:4])) ## Construct SMIset from named vector as(smivec, "SMIset") ## Assigning compound IDs and keeping them unique unique_ids <- makeUnique(cid(smiset)) cid(smiset) <- unique_ids ## Export SMIset to SMILES file # write.SMI(smiset[1:4], file="sub.smi", cid=TRUE)
Returns the status of a launched ChemMine Tools job as represented by a jobToken object.
status(object)status(object)
object |
A |
The status of the specified job is returned as a string. Possible values include "RUNNING", "FINISHED", or "FAILED".
Tyler William H Backman
See ChemMine Tools at http://chemmine.ucr.edu.
Functions: toolDetails, listCMTools, launchCMTool, browseJob, result
## Not run: ## list available tools listCMTools() ## get detailed instructions on using a tool toolDetails("Fingerprint Search") ## download compound 2244 from PubChem job1 <- launchCMTool("pubchemID2SDF", 2244) ## check job status and download result status(job1) result1 <- result(job1) ## End(Not run)## Not run: ## list available tools listCMTools() ## get detailed instructions on using a tool toolDetails("Fingerprint Search") ## download compound 2244 from PubChem job1 <- launchCMTool("pubchemID2SDF", 2244) ## check job status and download result status(job1) result1 <- result(job1) ## End(Not run)
Connects to the ChemMine Tools web service, and provides detailed instructions and example function calls for any tool.
toolDetails(tool_name)toolDetails(tool_name)
tool_name |
A tool name matching verbatim an existing tool name as listed by |
Prints instructions to console.
Tyler William H Backman
See ChemMine Tools at http://chemmine.ucr.edu.
Functions: launchCMTool, listCMTools, result, browseJob, status
## Not run: ## list available tools listCMTools() ## get detailed instructions on using a tool toolDetails("Fingerprint Search") ## download compound 2244 from PubChem job1 <- launchCMTool("pubchemID2SDF", 2244) ## check job status and download result status(job1) result1 <- result(job1) ## End(Not run)## Not run: ## list available tools listCMTools() ## get detailed instructions on using a tool toolDetails("Fingerprint Search") ## download compound 2244 from PubChem job1 <- launchCMTool("pubchemID2SDF", 2244) ## check job status and download result status(job1) result1 <- result(job1) ## End(Not run)
Further reduce the cutoff value of a nearest neighbor (NN) table, as produced by
nearestNeighbors. This allows one to compute a very relaxed NN table
initially, and then quickly restrict it later without having to re-compute all the
similarities.
trimNeighbors(nnm, cutoff)trimNeighbors(nnm, cutoff)
nnm |
A nearest neighbor table, as produced by |
cutoff |
The new similarities cutoff value. All pairs with a similarity less than this value will be removed from the table. |
The return value has the same structure as nnm, with some neighbors
removed from the indexes and similarties entries.
Kevin Horan
jarvisPatrick
nearestNeighbors
data(sdfsample) ap = sdf2ap(sdfsample) nnm = nearestNeighbors(ap,numNbrs=20) nnm = trimNeighbors(nnm,cutoff=0.5) clustering = jarvisPatrick(nnm,k=2,mode="a1b")data(sdfsample) ap = sdf2ap(sdfsample) nnm = nearestNeighbors(ap,numNbrs=20) nnm = trimNeighbors(nnm,cutoff=0.5) clustering = jarvisPatrick(nnm,k=2,mode="a1b")
Performs validity check of SDFs stored in SDFset objects. Currently, the function tests whether the atom block and the bond block in each SDF component of an SDFset have at least Nabcol and Nbbcol columns (default is 3 for both). In additions, it tests for the presence of NA values in the atom and bond blocks. The function returns a logical vector with TRUE values for valid compounds and FALSE values for invalid ones.
validSDF(x, Nabcol = 3, Nbbcol = 3, logic = "&", checkNA=TRUE)validSDF(x, Nabcol = 3, Nbbcol = 3, logic = "&", checkNA=TRUE)
x |
|
Nabcol |
minimum number of columns in atom block |
Nbbcol |
minimum number of columns in bond block |
logic |
logical connection (& or |) among Nabcol and Nbbcol cutoffs |
checkNA |
checks for NA values in atom and bond blocks |
The function is important to remove invalid compounds from SDFset containers.
logical vector of length x with TRUE for valid compounds and FALSE for invalid compounds.
Thomas Girke
...
Functions: read.SDFset
## SDFset instance data(sdfsample) sdfset <- sdfsample ## Detect and remove invalid SDFs in SDFset. valid <- validSDF(sdfset) which(!valid) # Returns index for invalid SDFs sdfset <- sdfset[valid] # Returns only valid SDFs.## SDFset instance data(sdfsample) sdfset <- sdfsample ## Detect and remove invalid SDFs in SDFset. valid <- validSDF(sdfset) which(!valid) # Returns index for invalid SDFs sdfset <- sdfset[valid] # Returns only valid SDFs.
Convenience function for viewing the content of complex objects like SDFset and APset containers. The function
is a shorthand wrapper for as(sdfset, "SDF") and as(apset, "AP").
view(x)view(x)
x |
object of class |
...
List populated with SDF and AP components.
Thomas Girke
...
Classes: SDF, SDFset, AP, APset
## Viewing content of SDFset data(sdfsample); sdfset <- sdfsample view(sdfset[1:4]) ## Viewing content of APset apset <- sdf2ap(sdfset[1:10]) view(apset)## Viewing content of SDFset data(sdfsample); sdfset <- sdfsample view(sdfset[1:4]) ## Viewing content of APset apset <- sdf2ap(sdfset[1:10]) view(apset)
Writes one or many molecules stored in a SDFset, SDFstr or SDF object to SD file.
write.SDF(sdf, file, cid = FALSE, ...)write.SDF(sdf, file, cid = FALSE, ...)
sdf |
object of class |
file |
name of SD file to write to |
cid |
if |
... |
the optional arguments of the |
If the write.SDF function is supplied with an SDFset object, then it uses internally the sdf2str function to allow customizing the resulting SD file. For this all optional arguments of the sdf2str function can be passed on to write.SDF.
Thomas Girke
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Import function: read.SDFset, read.SDFstr
## Instance of SDFset class data(sdfsample); sdfset <- sdfsample ## Write objects of classes SDFset/SDFstr/SDF to file # write.SDF(sdfset[1:4], file="sub.sdf") ## Example for writing customized SDFset to file containing ## ChemmineR signature, IDs from SDFset and no data block # write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE, db=NULL) ## Example for injecting a custom matrix/data frame into the data block of an ## SDFset and then writing it to an SD file props <- data.frame(MF=MF(sdfset), MW=MW(sdfset), atomcountMA(sdfset)) datablock(sdfset) <- props view(sdfset[1:4]) # write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE)## Instance of SDFset class data(sdfsample); sdfset <- sdfsample ## Write objects of classes SDFset/SDFstr/SDF to file # write.SDF(sdfset[1:4], file="sub.sdf") ## Example for writing customized SDFset to file containing ## ChemmineR signature, IDs from SDFset and no data block # write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE, db=NULL) ## Example for injecting a custom matrix/data frame into the data block of an ## SDFset and then writing it to an SD file props <- data.frame(MF=MF(sdfset), MW=MW(sdfset), atomcountMA(sdfset)) datablock(sdfset) <- props view(sdfset[1:4]) # write.SDF(sdfset[1:4], file="sub.sdf", sig=TRUE, cid=TRUE)
Splits SD Files into any number of smaller SD Files
write.SDFsplit(x, filetag, nmol)write.SDFsplit(x, filetag, nmol)
x |
object of class |
filetag |
string to prepend to file names |
nmol |
integer specifying number of molecules in split SD files |
To split an SD File into smaller ones, one can read the source file into R with read.SDFstr
and write out smaller ones with write.SDFsplit. Note: when importing big SD Files, read.SDFstr
will be much faster than read.SDFset, and there is no need to go through an SDFset object instance
in this case.
Thomas Girke
SDF format definition: http://www.symyx.com/downloads/public/ctfile/ctfile.jsp
Import/export functions: read.SDFset, read.SDFstr, read.SDFstr, read.SDFset
## Load sample data library(ChemmineR) data(sdfsample) ## Not run: ## Create sample SD File with 100 molecules write.SDF(sdfsample, "test.sdf") ## Read in sample SD File sdfstr <- read.SDFstr("test.sdf") ## Run export on SDFstr object write.SDFsplit(x=sdfstr, filetag="myfile", nmol=10) ## Run export on SDFset object write.SDFsplit(x=sdfsample, filetag="myfile", nmol=10) ## End(Not run)## Load sample data library(ChemmineR) data(sdfsample) ## Not run: ## Create sample SD File with 100 molecules write.SDF(sdfsample, "test.sdf") ## Read in sample SD File sdfstr <- read.SDFstr("test.sdf") ## Run export on SDFstr object write.SDFsplit(x=sdfstr, filetag="myfile", nmol=10) ## Run export on SDFset object write.SDFsplit(x=sdfsample, filetag="myfile", nmol=10) ## End(Not run)
Writes one or many molecules stored in a SMIset object to a SMILES file.
write.SMI(smi, file, cid = TRUE, ...)write.SMI(smi, file, cid = TRUE, ...)
smi |
object of class |
file |
name of SMILES file to write to |
cid |
if |
... |
option to pass on additional arguments |
...
Thomas Girke
SMILES (Simplified molecular-input line-entry system) format definition: http://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system
Functions: write.SDF
## Instance of SMIset class data(smisample); smiset <- smisample ## Write objects of classes SMIset to file with and ## without compound identifiers # write.SMI(smiset[1:4], file="sub.smi", cid=TRUE) # write.SMI(smiset[1:4], file="sub.smi", cid=FALSE)## Instance of SMIset class data(smisample); smiset <- smisample ## Write objects of classes SMIset to file with and ## without compound identifiers # write.SMI(smiset[1:4], file="sub.smi", cid=TRUE) # write.SMI(smiset[1:4], file="sub.smi", cid=FALSE)