Package 'Rcpi'

Description

2D Autocorrelations Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the 2D autocorrelations descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AA2DACOR data

Examples

data(AA2DACOR)

Description

3D-MoRSE Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the 3D-MoRSE descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AA3DMoRSE data

Examples

data(AA3DMoRSE)

Description

Atom-Centred Fragments Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the atom-centred fragments descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AAACF data

Examples

data(AAACF)

Description

BLOSUM100 Matrix for 20 Amino Acids

Details

BLOSUM100 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Value

AABLOSUM100 data

Examples

data(AABLOSUM100)

Description

BLOSUM45 Matrix for 20 Amino Acids

Details

BLOSUM45 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Value

AABLOSUM45 data

Examples

data(AABLOSUM45)

Description

BLOSUM50 Matrix for 20 Amino Acids

Details

BLOSUM50 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Value

AABLOSUM50 data

Examples

data(AABLOSUM50)

Description

BLOSUM62 Matrix for 20 Amino Acids

Details

BLOSUM62 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Value

AABLOSUM62 data

Examples

data(AABLOSUM62)

Description

BLOSUM80 Matrix for 20 Amino Acids

Details

BLOSUM80 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Value

AABLOSUM80 data

Examples

data(AABLOSUM80)

Description

Burden Eigenvalues Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the Burden eigenvalues descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AABurden data

Examples

data(AABurden)

Description

Connectivity Indices Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the connectivity indices descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AAConn data

Examples

data(AAConn)

Description

Constitutional Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the constitutional descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AAConst data

Examples

data(AAConst)

Description

CPSA Descriptors for 20 Amino Acids calculated by Discovery Studio

Details

This dataset includes the CPSA descriptors of the 20 amino acids calculated by Discovery Studio (version 2.5) used for scales extraction in this package. All amino acid molecules had also been optimized with MOE 2011.10 (semiempirical AM1) before calculating these CPSA descriptors. The SDF file containing the information of the optimized amino acid molecules is included in this package. See OptAA3d for more information.

Value

AACPSA data

Examples

data(AACPSA)

Description

All 2D Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes all the 2D descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AADescAll data

Examples

data(AADescAll)

Description

Edge Adjacency Indices Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the edge adjacency indices descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AAEdgeAdj data

Examples

data(AAEdgeAdj)

Description

Eigenvalue-Based Indices Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the eigenvalue-based indices descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AAEigIdx data

Examples

data(AAEigIdx)

Description

Functional Group Counts Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the functional group counts descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AAFGC data

Examples

data(AAFGC)

Description

Geometrical Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the geometrical descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AAGeom data

Examples

data(AAGeom)

Description

GETAWAY Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the GETAWAY descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AAGETAWAY data

Examples

data(AAGETAWAY)

Description

AAindex Data of 544 Physicochemical and Biological Properties for 20 Amino Acids

Details

The data was extracted from the AAindex1 database ver 9.1 (ftp://ftp.genome.jp/pub/db/community/aaindex/aaindex1) as of November 2012 (Data Last Modified 2006-08-14).

With this data, users could investigate each property's accession number and other details. Visit https://www.genome.jp/dbget/aaindex.html for more information.

Value

AAindex data

Examples

data(AAindex)

Description

Information Indices Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the information indices descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AAInfo data

Examples

data(AAInfo)

Description

Meta Information for the 20 Amino Acids

Details

This dataset includes the meta information of the 20 amino acids used for the 2D and 3D descriptor calculation in this package. Each column represents:

AAName

Amino acid name

Short

One-letter representation

Abbreviation

Three-letter representation

mol

SMILES representation

PUBCHEM_COMPOUND_CID

PubChem CID for the amino acid

PUBCHEM_LINK

PubChem link for the amino acid

Value

AAMetaInfo data

Examples

data(AAMetaInfo)

Description

2D Descriptors for 20 Amino Acids calculated by MOE 2011.10

Details

This dataset includes the 2D descriptors of the 20 amino acids calculated by MOE 2011.10 used for scales extraction in this package.

Value

AAMOE2D data

Examples

data(AAMOE2D)

Description

3D Descriptors for 20 Amino Acids calculated by MOE 2011.10

Details

This dataset includes the 3D descriptors of the 20 amino acids calculated by MOE 2011.10 used for scales extraction in this package. All amino acid molecules had also been optimized with MOE (semiempirical AM1) before calculating these 3D descriptors. The SDF file containing the information of the optimized amino acid molecules is included in this package. See OptAA3d for more information.

Value

AAMOE3D data

Examples

data(AAMOE3D)

Description

Molecular Properties Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the molecular properties descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AAMolProp data

Examples

data(AAMolProp)

Description

PAM120 Matrix for 20 Amino Acids

Details

PAM120 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Value

AAPAM120 data

Examples

data(AAPAM120)

Description

PAM250 Matrix for 20 Amino Acids

Details

PAM250 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Value

AAPAM250 data

Examples

data(AAPAM250)

Description

PAM30 Matrix for 20 Amino Acids

Details

PAM30 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Value

AAPAM30 data

Examples

data(AAPAM30)

Description

PAM40 Matrix for 20 Amino Acids

Details

PAM40 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Value

AAPAM40 data

Examples

data(AAPAM40)

Description

PAM70 Matrix for 20 Amino Acids

Details

PAM70 Matrix for the 20 amino acids. The matrix was extracted from the Biostrings package of Bioconductor.

Value

AAPAM70 data

Examples

data(AAPAM70)

Description

Randic Molecular Profiles Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the Randic molecular profiles descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AARandic data

Examples

data(AARandic)

Description

RDF Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the RDF descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AARDF data

Examples

data(AARDF)

Description

Topological Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the topological descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AATopo data

Examples

data(AATopo)

Description

Topological Charge Indices Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the topological charge indices descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AATopoChg data

Examples

data(AATopoChg)

Description

Walk and Path Counts Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the walk and path counts descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AAWalk data

Examples

data(AAWalk)

Description

WHIM Descriptors for 20 Amino Acids calculated by Dragon

Details

This dataset includes the WHIM descriptors of the 20 amino acids calculated by Dragon (version 5.4) used for scales extraction in this package.

Value

AAWHIM data

Examples

data(AAWHIM)

Description

Calculates auto covariance and auto cross covariance for generating scale-based descriptors of the same length.

Usage

acc(mat, lag)

Arguments

Value

A length lag * p^2 named vector, the element names are constructed by: the scales index (crossed scales index) and lag index.

Note

To see more details about auto cross covariance, check the references.

References

Wold, S., Jonsson, J., Sjörström, M., Sandberg, M., & Rännar, S. (1993). DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Analytica chimica acta, 277(2), 239–253.

Sjöström, M., Rännar, S., & Wieslander, Å. (1995). Polypeptide sequence property relationships in Escherichia coli based on auto cross covariances. Chemometrics and intelligent laboratory systems, 29(2), 295–305.

See Also

See extractPCMScales for generalized scales-based descriptors. For more details, see extractPCMDescScales and extractPCMPropScales.

Examples

p = 8    # p is the scales number
n = 200  # n is the amino acid number
lag = 7  # lag parameter
mat = matrix(rnorm(p * n), nrow = p, ncol = n)
acc(mat, lag)

Description

Calculate Drug Molecule Similarity Derived by Molecular Fingerprints

Usage

calcDrugFPSim(
  fp1,
  fp2,
  fptype = c("compact", "complete"),
  metric = c("tanimoto", "euclidean", "cosine", "dice", "hamming")
)

Arguments

Details

This function calculate drug molecule fingerprints similarity. Define a as the features of object A, b is the features of object B, c is the number of common features to A and B:

• Tanimoto: aka Jaccard - $c/a+b+c$

• Euclidean: $\sqrt(a + b)$

• Dice: aka Sorensen, Czekanowski, Hodgkin-Richards - $c/0.5[(a+c) + (b+c)]$

• Cosine: aka Ochiai, Carbo - $c/\sqrt((a + c)(b + c))$

• Hamming: aka Manhattan, taxi-cab, city-block distance - $(a + b)$

Value

The numeric similarity value.

References

Gasteiger, Johann, and Thomas Engel, eds. Chemoinformatics. Wiley.com, 2006.

Examples

mols = readMolFromSDF(system.file('compseq/tyrphostin.sdf', package = 'Rcpi'))

fp1 = extractDrugEstate(mols[[1]])
fp2 = extractDrugEstate(mols[[2]])
calcDrugFPSim(fp1, fp2, fptype = 'compact', metric = 'tanimoto')
calcDrugFPSim(fp1, fp2, fptype = 'compact', metric = 'euclidean')
calcDrugFPSim(fp1, fp2, fptype = 'compact', metric = 'cosine')
calcDrugFPSim(fp1, fp2, fptype = 'compact', metric = 'dice')
calcDrugFPSim(fp1, fp2, fptype = 'compact', metric = 'hamming')

fp3 = extractDrugEstateComplete(mols[[1]])
fp4 = extractDrugEstateComplete(mols[[2]])
calcDrugFPSim(fp3, fp4, fptype = 'complete', metric = 'tanimoto')
calcDrugFPSim(fp3, fp4, fptype = 'complete', metric = 'euclidean')
calcDrugFPSim(fp3, fp4, fptype = 'complete', metric = 'cosine')
calcDrugFPSim(fp3, fp4, fptype = 'complete', metric = 'dice')
calcDrugFPSim(fp3, fp4, fptype = 'complete', metric = 'hamming')

Description

Calculate Drug Molecule Similarity Derived by Maximum Common Substructure Search

Usage

calcDrugMCSSim(
  mol1,
  mol2,
  type = c("smile", "sdf"),
  plot = FALSE,
  al = 0,
  au = 0,
  bl = 0,
  bu = 0,
  matching.mode = "static",
  ...
)

Arguments

Details

This function calculate drug molecule similarity derived by maximum common substructure search. The maximum common substructure search algorithm is provided by the fmcsR package.

Value

A list containing the detail MCS information and similarity values. The numeric similarity value includes Tanimoto coefficient and overlap coefficient.

References

Wang, Y., Backman, T. W., Horan, K., & Girke, T. (2013). fmcsR: mismatch tolerant maximum common substructure searching in R. Bioinformatics, 29(21), 2792–2794.

Examples

mol1 = 'CC(C)CCCCCC(=O)NCC1=CC(=C(C=C1)O)OC'
mol2 = 'O=C(NCc1cc(OC)c(O)cc1)CCCC/C=C/C(C)C'
mol3 = readChar(system.file('compseq/DB00859.sdf', package = 'Rcpi'), nchars = 1e+6)
mol4 = readChar(system.file('compseq/DB00860.sdf', package = 'Rcpi'), nchars = 1e+6)
## Not run: 
sim1 = calcDrugMCSSim(mol1, mol2, type = 'smile')
sim2 = calcDrugMCSSim(mol3, mol4, type = 'sdf', plot = TRUE)
print(sim1[[2]])  # Tanimoto Coefficient
print(sim2[[3]])  # Overlap Coefficient

## End(Not run)

Description

Protein Sequence Similarity Calculation based on Gene Ontology (GO) Similarity

Usage

calcParProtGOSim(
  golist,
  type = c("go", "gene"),
  ont = c("MF", "BP", "CC"),
  organism = "human",
  measure = "Resnik",
  combine = "BMA"
)

Arguments

Details

This function calculates protein sequence similarity based on Gene Ontology (GO) similarity.

Value

A n x n similarity matrix.

See Also

See calcTwoProtGOSim for calculating the GO semantic similarity between two groups of GO terms or two Entrez gene IDs. See calcParProtSeqSim for paralleled protein similarity calculation based on sequence alignment.

Examples

# By GO Terms
go1 = c('GO:0005215', 'GO:0005488', 'GO:0005515', 'GO:0005625', 'GO:0005802', 'GO:0005905')  # AP4B1
go2 = c('GO:0005515', 'GO:0005634', 'GO:0005681', 'GO:0008380', 'GO:0031202')  # BCAS2
go3 = c('GO:0003735', 'GO:0005622', 'GO:0005840', 'GO:0006412')  # PDE4DIP
glist = list(go1, go2, go3)
calcParProtGOSim(glist, type = 'go', ont = 'CC', measure = 'Wang')

# By Entrez gene id
genelist = list(c('150', '151', '152', '1814', '1815', '1816'))
calcParProtGOSim(genelist, type = 'gene', ont = 'BP', measure = 'Wang')

Description

Parallellized Protein Sequence Similarity Calculation based on Sequence Alignment

Usage

calcParProtSeqSim(protlist, cores = 2, type = "local", submat = "BLOSUM62")

Arguments

Details

This function implemented the parallellized version for calculating protein sequence similarity based on sequence alignment.

Value

A n x n similarity matrix.

See Also

See calcTwoProtSeqSim for protein sequence alignment for two protein sequences. See calcParProtGOSim for protein similarity calculation based on Gene Ontology (GO) semantic similarity.

Examples

s1 = readFASTA(system.file('protseq/P00750.fasta', package = 'Rcpi'))[[1]]
s2 = readFASTA(system.file('protseq/P08218.fasta', package = 'Rcpi'))[[1]]
s3 = readFASTA(system.file('protseq/P10323.fasta', package = 'Rcpi'))[[1]]
s4 = readFASTA(system.file('protseq/P20160.fasta', package = 'Rcpi'))[[1]]
s5 = readFASTA(system.file('protseq/Q9NZP8.fasta', package = 'Rcpi'))[[1]]
plist = list(s1, s2, s3, s4, s5)

psimmat = calcParProtSeqSim(plist, cores = 2, type = 'local',
                            submat = 'BLOSUM62')
print(psimmat)

Description

Protein Similarity Calculation based on Gene Ontology (GO) Similarity

Usage

calcTwoProtGOSim(
  id1,
  id2,
  type = c("go", "gene"),
  ont = c("MF", "BP", "CC"),
  organism = "human",
  measure = "Resnik",
  combine = "BMA"
)

Arguments

Details

This function calculates the Gene Ontology (GO) similarity between two groups of GO terms or two Entrez gene IDs.

Value

A n x n matrix.

See Also

See calcParProtGOSim for protein similarity calculation based on Gene Ontology (GO) semantic similarity. See calcParProtSeqSim for paralleled protein similarity calculation based on sequence alignment.

Examples

# By GO terms
go1 = c("GO:0004022", "GO:0004024", "GO:0004023")
go2 = c("GO:0009055", "GO:0020037")
calcTwoProtGOSim(go1, go2, type = 'go', ont = 'MF', measure = 'Wang')

# By Entrez gene id
gene1 = '241'
gene2 = '251'
calcTwoProtGOSim(gene1, gene2, type = 'gene', ont = 'CC', measure = 'Lin')

Description

Protein Sequence Alignment for Two Protein Sequences

Usage

calcTwoProtSeqSim(seq1, seq2, type = "local", submat = "BLOSUM62")

Arguments

Details

This function implements the sequence alignment between two protein sequences.

Value

An Biostrings object containing the scores and other alignment information.

See Also

See calcParProtSeqSim for paralleled pairwise protein similarity calculation based on sequence alignment. See calcTwoProtGOSim for calculating the GO semantic similarity between two groups of GO terms or two Entrez gene IDs.

Examples

s1 = readFASTA(system.file('protseq/P00750.fasta', package = 'Rcpi'))[[1]]
s2 = readFASTA(system.file('protseq/P10323.fasta', package = 'Rcpi'))[[1]]

seqalign = calcTwoProtSeqSim(s1, s2)
seqalign
slot(seqalign, "score")

Description

Check if the protein sequence's amino acid types are the 20 default types

Usage

checkProt(x)

Arguments

Details

This function checks if the protein sequence's amino acid types are the 20 default types.

Value

Logical. TRUE if all of the amino acid types of the sequence are within the 20 default types.

Examples

x = readFASTA(system.file('protseq/P00750.fasta', package = 'Rcpi'))[[1]]
checkProt(x)  # TRUE
checkProt(paste(x, 'Z', sep = ''))  # FALSE

Description

Chemical File Formats Conversion

Usage

convMolFormat(infile, outfile, from, to)

Arguments

Details

This function converts between various chemical file formats via OpenBabel. The complete supported file format list could be found at https://openbabel.org/docs/FileFormats/Overview.html.

Value

NULL

Note

The supported formats include:

• abinit – ABINIT Output Format [Read-only]

• acr – ACR format [Read-only]

• adf – ADF cartesian input format [Write-only]

• adfout – ADF output format [Read-only]

• alc – Alchemy format

• arc – Accelrys/MSI Biosym/Insight II CAR format [Read-only]

• axsf – XCrySDen Structure Format [Read-only]

• bgf – MSI BGF format

• box – Dock 3.5 Box format

• bs – Ball and Stick format

• c3d1 – Chem3D Cartesian 1 format

• c3d2 – Chem3D Cartesian 2 format

• cac – CAChe MolStruct format [Write-only]

• caccrt – Cacao Cartesian format

• cache – CAChe MolStruct format [Write-only]

• cacint – Cacao Internal format [Write-only]

• can – Canonical SMILES format

• car – Accelrys/MSI Biosym/Insight II CAR format [Read-only]

• castep – CASTEP format [Read-only]

• ccc – CCC format [Read-only]

• cdx – ChemDraw binary format [Read-only]

• cdxml – ChemDraw CDXML format

• cht – Chemtool format [Write-only]

• cif – Crystallographic Information File

• ck – ChemKin format

• cml – Chemical Markup Language

• cmlr – CML Reaction format

• com – Gaussian 98/03 Input [Write-only]

• CONFIG – DL-POLY CONFIG

• CONTCAR – VASP format [Read-only]

• copy – Copy raw text [Write-only]

• crk2d – Chemical Resource Kit diagram(2D)

• crk3d – Chemical Resource Kit 3D format

• csr – Accelrys/MSI Quanta CSR format [Write-only]

• cssr – CSD CSSR format [Write-only]

• ct – ChemDraw Connection Table format

• cub – Gaussian cube format

• cube – Gaussian cube format

• dat – Generic Output file format [Read-only]

• dmol – DMol3 coordinates format

• dx – OpenDX cube format for APBS

• ent – Protein Data Bank format

• fa – FASTA format

• fasta – FASTA format

• fch – Gaussian formatted checkpoint file format [Read-only]

• fchk – Gaussian formatted checkpoint file format [Read-only]

• fck – Gaussian formatted checkpoint file format [Read-only]

• feat – Feature format

• fh – Fenske-Hall Z-Matrix format [Write-only]

• fhiaims – FHIaims XYZ format

• fix – SMILES FIX format [Write-only]

• fpt – Fingerprint format [Write-only]

• fract – Free Form Fractional format

• fs – Fastsearch format

• fsa – FASTA format

• g03 – Gaussian Output [Read-only]

• g09 – Gaussian Output [Read-only]

• g92 – Gaussian Output [Read-only]

• g94 – Gaussian Output [Read-only]

• g98 – Gaussian Output [Read-only]

• gal – Gaussian Output [Read-only]

• gam – GAMESS Output [Read-only]

• gamess – GAMESS Output [Read-only]

• gamin – GAMESS Input

• gamout – GAMESS Output [Read-only]

• gau – Gaussian 98/03 Input [Write-only]

• gjc – Gaussian 98/03 Input [Write-only]

• gjf – Gaussian 98/03 Input [Write-only]

• got – GULP format [Read-only]

• gpr – Ghemical format

• gr96 – GROMOS96 format [Write-only]

• gro – GRO format

• gukin – GAMESS-UK Input

• gukout – GAMESS-UK Output

• gzmat – Gaussian Z-Matrix Input

• hin – HyperChem HIN format

• HISTORY – DL-POLY HISTORY [Read-only]

• inchi – InChI format

• inchikey – InChIKey [Write-only]

• inp – GAMESS Input

• ins – ShelX format [Read-only]

• jin – Jaguar input format [Write-only]

• jout – Jaguar output format [Read-only]

• k – Compare molecules using InChI [Write-only]

• log – Generic Output file format [Read-only]

• mcdl – MCDL format

• mcif – Macromolecular Crystallographic Info

• mdl – MDL MOL format

• ml2 – Sybyl Mol2 format

• mmcif – Macromolecular Crystallographic Info

• mmd – MacroModel format

• mmod – MacroModel format

• mna – Multilevel Neighborhoods of Atoms (MNA) [Write-only]

• mol – MDL MOL format

• mol2 – Sybyl Mol2 format

• mold – Molden format

• molden – Molden format

• molf – Molden format

• molreport – Open Babel molecule report [Write-only]

• moo – MOPAC Output format [Read-only]

• mop – MOPAC Cartesian format

• mopcrt – MOPAC Cartesian format

• mopin – MOPAC Internal

• mopout – MOPAC Output format [Read-only]

• mp – Molpro input format [Write-only]

• mpc – MOPAC Cartesian format

• mpd – MolPrint2D format [Write-only]

• mpo – Molpro output format [Read-only]

• mpqc – MPQC output format [Read-only]

• mpqcin – MPQC simplified input format [Write-only]

• mrv – Chemical Markup Language

• msi – Accelrys/MSI Cerius II MSI format [Read-only]

• msms – M.F. Sanner's MSMS input format [Write-only]

• nul – Outputs nothing [Write-only]

• nw – NWChem input format [Write-only]

• nwo – NWChem output format [Read-only]

• out – Generic Output file format [Read-only]

• outmol – DMol3 coordinates format

• output – Generic Output file format [Read-only]

• pc – PubChem format [Read-only]

• pcm – PCModel Format

• pdb – Protein Data Bank format

• pdbqt – AutoDock PDQBT format

• png – PNG 2D depiction

• POSCAR – VASP format [Read-only]

• pov – POV-Ray input format [Write-only]

• pqr – PQR format

• pqs – Parallel Quantum Solutions format

• prep – Amber Prep format [Read-only]

• pwscf – PWscf format [Read-only]

• qcin – Q-Chem input format [Write-only]

• qcout – Q-Chem output format [Read-only]

• report – Open Babel report format [Write-only]

• res – ShelX format [Read-only]

• rsmi – Reaction SMILES format

• rxn – MDL RXN format

• sd – MDL MOL format

• sdf – MDL MOL format

• smi – SMILES format

• smiles – SMILES format

• svg – SVG 2D depiction [Write-only]

• sy2 – Sybyl Mol2 format

• t41 – ADF TAPE41 format [Read-only]

• tdd – Thermo format

• text – Read and write raw text

• therm – Thermo format

• tmol – TurboMole Coordinate format

• txt – Title format

• txyz – Tinker XYZ format

• unixyz – UniChem XYZ format

• vmol – ViewMol format

• xed – XED format [Write-only]

• xml – General XML format [Read-only]

• xsf – XCrySDen Structure Format [Read-only]

• xyz – XYZ cartesian coordinates format

• yob – YASARA.org YOB format

• zin – ZINDO input format [Write-only]

Examples

sdf = system.file('sysdata/OptAA3d.sdf', package = 'Rcpi')
# SDF to SMILES
## Not run: 
convMolFormat(infile = sdf, outfile = 'aa.smi',
              from = 'sdf', to = 'smiles')
## End(Not run)
# SMILES to MOPAC Cartesian format
## Not run: 
convMolFormat(infile = 'aa.smi', outfile = 'aa.mop',
              from = 'smiles', to = 'mop')
## End(Not run)

Description

Calculate All Molecular Descriptors in Rcpi at Once

Usage

extractDrugAIO(molecules, silent = TRUE, warn = TRUE)

Arguments

Details

This function calculates all the molecular descriptors in the Rcpi package at once.

Value

A data frame, each row represents one of the molecules, each column represents one descriptor. Currently, this function returns total 293 descriptors composed of 48 descriptor types.

Note

Note that we need 3-D coordinates of the molecules to calculate some of the descriptors, if not provided, these descriptors values will be NA.

Examples

# Load 20 small molecules that have 3D coordinates
sdf = system.file('sysdata/OptAA3d.sdf', package = 'Rcpi')

mol = readMolFromSDF(sdf)
dat = extractDrugAIO(mol, warn = FALSE)

Description

Calculate Atom Additive logP and Molar Refractivity Values Descriptor

Usage

extractDrugALOGP(molecules, silent = TRUE)

Arguments

Details

Calculates ALOGP (Ghose-Crippen LogKow) and the Ghose-Crippen molar refractivity as described by Ghose, A.K. and Crippen, G.M. Note the underlying code in CDK assumes that aromaticity has been detected before evaluating this descriptor. The code also expects that the molecule will have hydrogens explicitly set. For SD files, this is usually not a problem since hydrogens are explicit. But for the case of molecules obtained from SMILES, hydrogens must be made explicit.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns three columns named ALogP, ALogp2 and AMR.

References

Ghose, A.K. and Crippen, G.M. , Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships. I. Partition coefficients as a measure of hydrophobicity, Journal of Computational Chemistry, 1986, 7:565-577.

Ghose, A.K. and Crippen, G.M. , Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions, Journal of Chemical Information and Computer Science, 1987, 27:21-35.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugALOGP(mol)
head(dat)

Description

Calculate the Number of Amino Acids Descriptor

Usage

extractDrugAminoAcidCount(molecules, silent = TRUE)

Arguments

Details

Calculates the number of each amino acids (total 20 types) found in the molecues.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 20 columns named nA, nR, nN, nD, nC, nF, nQ, nE, nG, nH, nI, nP, nL nK, nM, nS, nT, nY nV, nW.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugAminoAcidCount(mol)
head(dat)

Description

Calculate the Sum of the Atomic Polarizabilities Descriptor

Usage

extractDrugApol(molecules, silent = TRUE)

Arguments

Details

Calculates the sum of the atomic polarizabilities (including implicit hydrogens) descriptor. Polarizabilities are taken from https://bit.ly/3PvNbhe.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named apol.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugApol(mol)
head(dat)

Description

Calculate the Number of Aromatic Atoms Descriptor

Usage

extractDrugAromaticAtomsCount(molecules, silent = TRUE)

Arguments

Details

Calculates the number of aromatic atoms of a molecule.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named naAromAtom.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugAromaticAtomsCount(mol)
head(dat)

Description

Calculate the Number of Aromatic Bonds Descriptor

Usage

extractDrugAromaticBondsCount(molecules, silent = TRUE)

Arguments

Details

Calculates the number of aromatic bonds of a molecule.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named nAromBond.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugAromaticBondsCount(mol)
head(dat)

Description

Calculate the Number of Atom Descriptor

Usage

extractDrugAtomCount(molecules, silent = TRUE)

Arguments

Details

Calculates the number of atoms of a certain element type in a molecule. By default it returns the count of all atoms.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named nAtom.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugAtomCount(mol)
head(dat)

Description

Calculate the Moreau-Broto Autocorrelation Descriptors using Partial Charges

Usage

extractDrugAutocorrelationCharge(molecules, silent = TRUE)

Arguments

Details

Calculates the ATS autocorrelation descriptor, where the weight equal to the charges.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 5 columns named ATSc1, ATSc2, ATSc3, ATSc4, ATSc5.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugAutocorrelationCharge(mol)
head(dat)

Description

Calculate the Moreau-Broto Autocorrelation Descriptors using Atomic Weight

Usage

extractDrugAutocorrelationMass(molecules, silent = TRUE)

Arguments

Details

Calculates the ATS autocorrelation descriptor, where the weight equal to the scaled atomic mass.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 5 columns named ATSm1, ATSm2, ATSm3, ATSm4, ATSm5.

References

Moreau, Gilles, and Pierre Broto. The autocorrelation of a topological structure: a new molecular descriptor. Nouv. J. Chim 4 (1980): 359-360.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugAutocorrelationMass(mol)
head(dat)

Description

Calculate the Moreau-Broto Autocorrelation Descriptors using Polarizability

Usage

extractDrugAutocorrelationPolarizability(molecules, silent = TRUE)

Arguments

Details

Calculates the ATS autocorrelation descriptor using polarizability.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 5 columns named ATSp1, ATSp2, ATSp3, ATSp4, ATSp5.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugAutocorrelationPolarizability(mol)
head(dat)

Description

BCUT – Eigenvalue Based Descriptor

Usage

extractDrugBCUT(molecules, silent = TRUE)

Arguments

Details

Eigenvalue based descriptor noted for its utility in chemical diversity. Described by Pearlman et al. The descriptor is based on a weighted version of the Burden matrix which takes into account both the connectivity as well as atomic properties of a molecule. The weights are a variety of atom properties placed along the diagonal of the Burden matrix. Currently three weighting schemes are employed:

• Atomic Weight

• Partial Charge (Gasteiger Marsilli)

• Polarizability (Kang et al.)

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 6 columns:

• BCUTw-1l, BCUTw-2l ... - n high lowest atom weighted BCUTS

• BCUTw-1h, BCUTw-2h ... - n low highest atom weighted BCUTS

• BCUTc-1l, BCUTc-2l ... - n high lowest partial charge weighted BCUTS

• BCUTc-1h, BCUTc-2h ... - n low highest partial charge weighted BCUTS

• BCUTp-1l, BCUTp-2l ... - n high lowest polarizability weighted BCUTS

• BCUTp-1h, BCUTp-2h ... - n low highest polarizability weighted BCUTS

Note

By default, the descriptor will return the highest and lowest eigenvalues for the three classes of descriptor in a single ArrayList (in the order shown above). However it is also possible to supply a parameter list indicating how many of the highest and lowest eigenvalues (for each class of descriptor) are required. The descriptor works with the hydrogen depleted molecule.

A side effect of specifying the number of highest and lowest eigenvalues is that it is possible to get two copies of all the eigenvalues. That is, if a molecule has 5 heavy atoms, then specifying the 5 highest eigenvalues returns all of them, and specifying the 5 lowest eigenvalues returns all of them, resulting in two copies of all the eigenvalues.

Note that it is possible to specify an arbitrarily large number of eigenvalues to be returned. However if the number (i.e., nhigh or nlow) is larger than the number of heavy atoms, the remaining eignevalues will be NaN.

Given the above description, if the aim is to gt all the eigenvalues for a molecule, you should set nlow to 0 and specify the number of heavy atoms (or some large number) for nhigh (or vice versa).

References

Pearlman, R.S. and Smith, K.M., Metric Validation and the Receptor-Relevant Subspace Concept, J. Chem. Inf. Comput. Sci., 1999, 39:28-35.

Burden, F.R., Molecular identification number for substructure searches, J. Chem. Inf. Comput. Sci., 1989, 29:225-227.

Burden, F.R., Chemically Intuitive Molecular Index, Quant. Struct. -Act. Relat., 1997, 16:309-314

Kang, Y.K. and Jhon, M.S., Additivity of Atomic Static Polarizabilities and Dispersion Coefficients, Theoretica Chimica Acta, 1982, 61:41-48

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugBCUT(mol)
head(dat)

Description

Calculate the Descriptor Based on the Number of Bonds of a Certain Bond Order

Usage

extractDrugBondCount(molecules, silent = TRUE)

Arguments

Details

Calculates the descriptor based on the number of bonds of a certain bond order.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named nB.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugBondCount(mol)
head(dat)

Description

Calculates the Descriptor that Describes the Sum of the Absolute Value of the Difference between Atomic Polarizabilities of All Bonded Atoms in the Molecule

Usage

extractDrugBPol(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the sum of the absolute value of the difference between atomic polarizabilities of all bonded atoms in the molecule (including implicit hydrogens) with polarizabilities taken from https://bit.ly/3PvNbhe. This descriptor assumes 2-centered bonds.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named bpol.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugBPol(mol)
head(dat)

Description

Topological Descriptor Characterizing the Carbon Connectivity in Terms of Hybridization

Usage

extractDrugCarbonTypes(molecules, silent = TRUE)

Arguments

Details

Calculates the carbon connectivity in terms of hybridization. The function calculates 9 descriptors in the following order:

• C1SP1 - triply hound carbon bound to one other carbon

• C2SP1 - triply bound carbon bound to two other carbons

• C1SP2 - doubly hound carbon bound to one other carbon

• C2SP2 - doubly bound carbon bound to two other carbons

• C3SP2 - doubly bound carbon bound to three other carbons

• C1SP3 - singly bound carbon bound to one other carbon

• C2SP3 - singly bound carbon bound to two other carbons

• C3SP3 - singly bound carbon bound to three other carbons

• C4SP3 - singly bound carbon bound to four other carbons

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 9 columns named C1SP1, C2SP1, C1SP2, C2SP2, C3SP2, C1SP3, C2SP3, C3SP3 and C4SP3.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugCarbonTypes(mol)
head(dat)

Description

Calculate the Kier and Hall Chi Chain Indices of Orders 3, 4, 5, 6 and 7

Usage

extractDrugChiChain(molecules, silent = TRUE)

Arguments

Details

Evaluates chi chain descriptors. The code currently evluates the simple and valence chi chain descriptors of orders 3, 4, 5, 6 and 7. It utilizes the graph isomorphism code of the CDK to find fragments matching SMILES strings representing the fragments corresponding to each type of chain.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 10 columns, in the following order:

• SCH.3 - Simple chain, order 3

• SCH.4 - Simple chain, order 4

• SCH.5 - Simple chain, order 5

• SCH.6 - Simple chain, order 6

• SCH.7 - Simple chain, order 7

• VCH.3 - Valence chain, order 3

• VCH.4 - Valence chain, order 4

• VCH.5 - Valence chain, order 5

• VCH.6 - Valence chain, order 6

• VCH.7 - Valence chain, order 7

Note

These descriptors are calculated using graph isomorphism to identify the various fragments. As a result calculations may be slow. In addition, recent versions of Molconn-Z use simplified fragment definitions (i.e., rings without branches etc.) whereas these descriptors use the older more complex fragment definitions.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugChiChain(mol)
head(dat)

Description

Evaluates the Kier and Hall Chi cluster indices of orders 3, 4, 5 and 6

Usage

extractDrugChiCluster(molecules, silent = TRUE)

Arguments

Details

Evaluates chi cluster descriptors. It utilizes the graph isomorphism code of the CDK to find fragments matching SMILES strings representing the fragments corresponding to each type of chain.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 8 columns, the order and names of the columns returned is:

• SC.3 - Simple cluster, order 3

• SC.4 - Simple cluster, order 4

• SC.5 - Simple cluster, order 5

• SC.6 - Simple cluster, order 6

• VC.3 - Valence cluster, order 3

• VC.4 - Valence cluster, order 4

• VC.5 - Valence cluster, order 5

• VC.6 - Valence cluster, order 6

Note

These descriptors are calculated using graph isomorphism to identify the various fragments. As a result calculations may be slow. In addition, recent versions of Molconn-Z use simplified fragment definitions (i.e., rings without branches etc.) whereas these descriptors use the older more complex fragment definitions.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugChiCluster(mol)
head(dat)

Description

Calculate the Kier and Hall Chi Path Indices of Orders 0 to 7

Usage

extractDrugChiPath(molecules, silent = TRUE)

Arguments

Details

Evaluates chi path descriptors. This function utilizes the graph isomorphism code of the CDK to find fragments matching SMILES strings representing the fragments corresponding to each type of chain.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 16 columns, The order and names of the columns returned is:

• SP.0, SP.1, ..., SP.7 - Simple path, orders 0 to 7

• VP.0, VP.1, ..., VP.7 - Valence path, orders 0 to 7

Note

These descriptors are calculated using graph isomorphism to identify the various fragments. As a result calculations may be slow. In addition, recent versions of Molconn-Z use simplified fragment definitions (i.e., rings without branches etc.) whereas these descriptors use the older more complex fragment definitions.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugChiPath(mol)
head(dat)

Description

Calculate the Kier and Hall Chi Path Cluster Indices of Orders 4, 5 and 6

Usage

extractDrugChiPathCluster(molecules, silent = TRUE)

Arguments

Details

Evaluates chi path cluster descriptors. The code currently evluates the simple and valence chi chain descriptors of orders 4, 5 and 6. It utilizes the graph isomorphism code of the CDK to find fragments matching SMILES strings representing the fragments corresponding to each type of chain.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 6 columns named SPC.4, SPC.5, SPC.6, VPC.4, VPC.5, VPC.6:

• SPC.4 - Simple path cluster, order 4

• SPC.5 - Simple path cluster, order 5

• SPC.6 - Simple path cluster, order 6

• VPC.4 - Valence path cluster, order 4

• VPC.5 - Valence path cluster, order 5

• VPC.6 - Valence path cluster, order 6

Note

These descriptors are calculated using graph isomorphism to identify the various fragments. As a result calculations may be slow. In addition, recent versions of Molconn-Z use simplified fragment definitions (i.e., rings without branches etc.) whereas these descriptors use the older more complex fragment definitions.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugChiPathCluster(mol)
head(dat)

Description

A Variety of Descriptors Combining Surface Area and Partial Charge Information

Usage

extractDrugCPSA(molecules, silent = TRUE)

Arguments

Details

Calculates 29 Charged Partial Surface Area (CPSA) descriptors. The CPSA's were developed by Stanton et al.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 29 columns:

• PPSA.1 - partial positive surface area – sum of surface area on positive parts of molecule

• PPSA.2 - partial positive surface area * total positive charge on the molecule

• PPSA.3 - charge weighted partial positive surface area

• PNSA.1 - partial negative surface area – sum of surface area on negative parts of molecule

• PNSA.2 - partial negative surface area * total negative charge on the molecule

• PNSA.3 - charge weighted partial negative surface area

• DPSA.1 - difference of PPSA.1 and PNSA.1

• DPSA.2 - difference of FPSA.2 and PNSA.2

• DPSA.3 - difference of PPSA.3 and PNSA.3

• FPSA.1 - PPSA.1 / total molecular surface area

• FFSA.2 - PPSA.2 / total molecular surface area

• FPSA.3 - PPSA.3 / total molecular surface area

• FNSA.1 - PNSA.1 / total molecular surface area

• FNSA.2 - PNSA.2 / total molecular surface area

• FNSA.3 - PNSA.3 / total molecular surface area

• WPSA.1 - PPSA.1 * total molecular surface area / 1000

• WPSA.2 - PPSA.2 * total molecular surface area /1000

• WPSA.3 - PPSA.3 * total molecular surface area / 1000

• WNSA.1 - PNSA.1 * total molecular surface area /1000

• WNSA.2 - PNSA.2 * total molecular surface area / 1000

• WNSA.3 - PNSA.3 * total molecular surface area / 1000

• RPCG - relative positive charge – most positive charge / total positive charge

• RNCG - relative negative charge – most negative charge / total negative charge

• RPCS - relative positive charge surface area – most positive surface area * RPCG

• RNCS - relative negative charge surface area – most negative surface area * RNCG

• THSA - sum of solvent accessible surface areas of atoms with absolute value of partial charges less than 0.2

• TPSA - sum of solvent accessible surface areas of atoms with absolute value of partial charges greater than or equal 0.2

• RHSA - THSA / total molecular surface area

• RPSA - TPSA / total molecular surface area

References

Stanton, D.T. and Jurs, P.C. , Development and Use of Charged Partial Surface Area Structural Descriptors in Computer Assissted Quantitative Structure Property Relationship Studies, Analytical Chemistry, 1990, 62:2323.2329.

Examples

sdf = system.file('sysdata/OptAA3d.sdf', package = 'Rcpi')

mol = readMolFromSDF(sdf)
dat = extractDrugCPSA(mol)
head(dat)

Description

Calculate Molecular Descriptors Provided by OpenBabel

Usage

extractDrugDescOB(molecules, type = c("smile", "sdf"))

Arguments

Details

This function calculates 14 types of the numerical molecular descriptors provided in OpenBabel.

Value

A data frame, each row represents one of the molecules, each column represents one descriptor. This function returns 14 columns named abonds, atoms, bonds, dbonds, HBA1, HBA2, HBD, logP, MR, MW, nF, sbonds, tbonds, TPSA:

• abonds - Number of aromatic bonds

• atoms - Number of atoms

• bonds - Number of bonds

• dbonds - Number of double bonds

• HBA1 - Number of Hydrogen Bond Acceptors 1

• HBA2 - Number of Hydrogen Bond Acceptors 2

• HBD - Number of Hydrogen Bond Donors

• logP - Octanol/Water Partition Coefficient

• MR - Molar Refractivity

• MW - Molecular Weight Filter

• nF - Number of Fluorine Atoms

• sbonds - Number of single bonds

• tbonds - Number of triple bonds

• TPSA - Topological Polar Surface Area

Examples

mol1 = 'CC(=O)NCCC1=CNc2c1cc(OC)cc2'  # one molecule SMILE in a vector
mol2 = c('OCCc1c(C)[n+](=cs1)Cc2cnc(C)nc(N)2',
         'CCc(c1)ccc2[n+]1ccc3c2Nc4c3cccc4',
         '[Cu+2].[O-]S(=O)(=O)[O-]')  # multiple SMILEs in a vector
mol3 = readChar(system.file('compseq/DB00860.sdf', package = 'Rcpi'),
                nchars = 1e+6)  # single molecule in a sdf file
mol4 = readChar(system.file('sysdata/OptAA3d.sdf', package = 'Rcpi'),
                nchars = 1e+6)  # multiple molecules in a sdf file

## Not run: 
smidesc0 = extractDrugDescOB(mol1, type = 'smile')
smidesc1 = extractDrugDescOB(mol2, type = 'smile')
sdfdesc0 = extractDrugDescOB(mol3, type = 'sdf')
sdfdesc1 = extractDrugDescOB(mol4, type = 'sdf')
## End(Not run)

Description

Calculate the Eccentric Connectivity Index Descriptor

Usage

extractDrugECI(molecules, silent = TRUE)

Arguments

Details

Eccentric Connectivity Index (ECI) is a topological descriptor combining distance and adjacency information. This descriptor is described by Sharma et al. and has been shown to correlate well with a number of physical properties. The descriptor is also reported to have good discriminatory ability. The eccentric connectivity index for a hydrogen supressed molecular graph is given by

$x_i^c = \sum_{i = 1}^{n} E(i) V(i)$

where E(i) is the eccentricity of the i-th atom (path length from the i-th atom to the atom farthest from it) and V(i) is the vertex degree of the i-th atom.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named ECCEN.

References

Sharma, V. and Goswami, R. and Madan, A.K. (1997), Eccentric Connectivity Index: A Novel Highly Discriminating Topological Descriptor for Structure-Property and Structure-Activity Studies, Journal of Chemical Information and Computer Sciences, 37:273-282

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugECI(mol)
head(dat)

Description

Calculate the E-State Molecular Fingerprints (in Compact Format)

Usage

extractDrugEstate(molecules, silent = TRUE)

Arguments

Details

79 bit fingerprints corresponding to the E-State atom types described by Hall and Kier.

Value

A list, each component represents one of the molecules, each element in the component represents the index of which element in the fingerprint is 1. Each component's name is the length of the fingerprints.

See Also

extractDrugEstateComplete

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugEstate(mol)
head(fp)

Description

Calculate the E-State Molecular Fingerprints (in Complete Format)

Usage

extractDrugEstateComplete(molecules, silent = TRUE)

Arguments

Details

79 bit fingerprints corresponding to the E-State atom types described by Hall and Kier.

Value

An integer vector or a matrix. Each row represents one molecule, the columns represent the fingerprints.

See Also

extractDrugEstate

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugEstateComplete(mol)
dim(fp)

Description

Calculate the Extended Molecular Fingerprints (in Compact Format)

Usage

extractDrugExtended(molecules, depth = 6, size = 1024, silent = TRUE)

Arguments

Details

Calculate the extended molecular fingerprints. Considers paths of a given length, similar to the standard type, but takes rings and atomic properties into account into account. This is hashed fingerprints, with a default length of 1024.

Value

A list, each component represents one of the molecules, each element in the component represents the index of which element in the fingerprint is 1. Each component's name is the length of the fingerprints.

See Also

extractDrugExtendedComplete

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugExtended(mol)
head(fp)

Description

Calculate the Extended Molecular Fingerprints (in Complete Format)

Usage

extractDrugExtendedComplete(molecules, depth = 6, size = 1024, silent = TRUE)

Arguments

Details

Calculate the extended molecular fingerprints. Considers paths of a given length, similar to the standard type, but takes rings and atomic properties into account into account. This is hashed fingerprints, with a default length of 1024.

Value

An integer vector or a matrix. Each row represents one molecule, the columns represent the fingerprints.

See Also

extractDrugExtended

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugExtendedComplete(mol)
dim(fp)

Description

Calculate the FMF Descriptor

Usage

extractDrugFMF(molecules, silent = TRUE)

Arguments

Details

Calculates the FMF descriptor characterizing molecular complexity in terms of its Murcko framework. This descriptor is the ratio of heavy atoms in the framework to the total number of heavy atoms in the molecule. By definition, acyclic molecules which have no frameworks, will have a value of 0. Note that the authors consider an isolated ring system to be a framework (even though there is no linker).

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named FMF.

References

Yang, Y., Chen, H., Nilsson, I., Muresan, S., & Engkvist, O. (2010). Investigation of the relationship between topology and selectivity for druglike molecules. Journal of medicinal chemistry, 53(21), 7709-7714.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugFMF(mol)
head(dat)

Description

Calculate Complexity of a System

Usage

extractDrugFragmentComplexity(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the complexity of a system. The complexity is defined in Nilakantan, R. et al. as:

$C = abs(B^2 - A^2 + A) + \frac{H}{100}$

where C is complexity, A is the number of non-hydrogen atoms, B is the number of bonds and H is the number of heteroatoms.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named fragC.

References

Nilakantan, R. and Nunn, D.S. and Greenblatt, L. and Walker, G. and Haraki, K. and Mobilio, D., A family of ring system-based structural fragments for use in structure-activity studies: database mining and recursive partitioning., Journal of chemical information and modeling, 2006, 46:1069-1077

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugFragmentComplexity(mol)
head(dat)

Description

Calculate the Graph Molecular Fingerprints (in Compact Format)

Usage

extractDrugGraph(molecules, depth = 6, size = 1024, silent = TRUE)

Arguments

Details

Calculate the graph molecular fingerprints. Similar to the standard type by simply considers connectivity. This is hashed fingerprints, with a default length of 1024.

Value

A list, each component represents one of the molecules, each element in the component represents the index of which element in the fingerprint is 1. Each component's name is the length of the fingerprints.

See Also

extractDrugGraphComplete

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugGraph(mol)
head(fp)

Description

Calculate the Graph Molecular Fingerprints (in Complete Format)

Usage

extractDrugGraphComplete(molecules, depth = 6, size = 1024, silent = TRUE)

Arguments

Details

Calculate the graph molecular fingerprints. Similar to the standard type by simply considers connectivity. This is hashed fingerprints, with a default length of 1024.

Value

An integer vector or a matrix. Each row represents one molecule, the columns represent the fingerprints.

See Also

extractDrugGraph

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugGraphComplete(mol)
dim(fp)

Description

Descriptor Characterizing the Mass Distribution of the Molecule.

Usage

extractDrugGravitationalIndex(molecules, silent = TRUE)

Arguments

Details

Descriptor characterizing the mass distribution of the molecule described by Katritzky et al. For modelling purposes the value of the descriptor is calculated both with and without H atoms. Furthermore the square and cube roots of the descriptor are also generated as described by Wessel et al.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 9 columns:

• GRAV.1 - gravitational index of heavy atoms

• GRAV.2 - square root of gravitational index of heavy atoms

• GRAV.3 - cube root of gravitational index of heavy atoms

• GRAVH.1 - gravitational index - hydrogens included

• GRAVH.2 - square root of hydrogen-included gravitational index

• GRAVH.3 - cube root of hydrogen-included gravitational index

• GRAV.4 - grav1 for all pairs of atoms (not just bonded pairs)

• GRAV.5 - grav2 for all pairs of atoms (not just bonded pairs)

• GRAV.6 - grav3 for all pairs of atoms (not just bonded pairs)

References

Katritzky, A.R. and Mu, L. and Lobanov, V.S. and Karelson, M., Correlation of Boiling Points With Molecular Structure. 1. A Training Set of 298 Diverse Organics and a Test Set of 9 Simple Inorganics, J. Phys. Chem., 1996, 100:10400-10407.

Wessel, M.D. and Jurs, P.C. and Tolan, J.W. and Muskal, S.M. , Prediction of Human Intestinal Absorption of Drug Compounds From Molecular Structure, Journal of Chemical Information and Computer Sciences, 1998, 38:726-735.

Examples

sdf = system.file('sysdata/OptAA3d.sdf', package = 'Rcpi')

mol = readMolFromSDF(sdf)
dat = extractDrugGravitationalIndex(mol)
head(dat)

Description

Number of Hydrogen Bond Acceptors

Usage

extractDrugHBondAcceptorCount(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the number of hydrogen bond acceptors using a slightly simplified version of the PHACIR atom types. The following groups are counted as hydrogen bond acceptors: any oxygen where the formal charge of the oxygen is non-positive (i.e. formal charge <= 0) except

1. an aromatic ether oxygen (i.e. an ether oxygen that is adjacent to at least one aromatic carbon)

2. an oxygen that is adjacent to a nitrogen

and any nitrogen where the formal charge of the nitrogen is non-positive (i.e. formal charge <= 0) except a nitrogen that is adjacent to an oxygen.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named nHBAcc.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugHBondAcceptorCount(mol)
head(dat)

Description

Number of Hydrogen Bond Donors

Usage

extractDrugHBondDonorCount(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the number of hydrogen bond donors using a slightly simplified version of the PHACIR atom types (https://bit.ly/3qXQELf). The following groups are counted as hydrogen bond donors:

• Any-OH where the formal charge of the oxygen is non-negative (i.e. formal charge >= 0)

• Any-NH where the formal charge of the nitrogen is non-negative (i.e. formal charge >= 0)

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named nHBDon.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugHBondDonorCount(mol)
head(dat)

Description

Calculate the Hybridization Molecular Fingerprints (in Compact Format)

Usage

extractDrugHybridization(molecules, depth = 6, size = 1024, silent = TRUE)

Arguments

Details

Calculate the hybridization molecular fingerprints. Similar to the standard type, but only consider hybridization state. This is hashed fingerprints, with a default length of 1024.

Value

A list, each component represents one of the molecules, each element in the component represents the index of which element in the fingerprint is 1. Each component's name is the length of the fingerprints.

See Also

extractDrugHybridizationComplete

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugHybridization(mol)
head(fp)

Description

Calculate the Hybridization Molecular Fingerprints (in Complete Format)

Usage

extractDrugHybridizationComplete(
  molecules,
  depth = 6,
  size = 1024,
  silent = TRUE
)

Arguments

Details

Calculate the hybridization molecular fingerprints. Similar to the standard type, but only consider hybridization state. This is hashed fingerprints, with a default length of 1024.

Value

An integer vector or a matrix. Each row represents one molecule, the columns represent the fingerprints.

See Also

extractDrugHybridization

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugHybridizationComplete(mol)
dim(fp)

Description

Descriptor that Characterizing Molecular Complexity in Terms of Carbon Hybridization States

Usage

extractDrugHybridizationRatio(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the fraction of sp3 carbons to sp2 carbons. Note that it only considers carbon atoms and rather than use a simple ratio it reports the value of Nsp3/(Nsp3 + Nsp2). The original form of the descriptor (i.e., simple ratio) has been used to characterize molecular complexity, especially in the are of natural products, which usually have a high value of the sp3 to sp2 ratio.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named HybRatio.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugHybridizationRatio(mol)
head(dat)

Description

Calculate the Descriptor that Evaluates the Ionization Potential

Usage

extractDrugIPMolecularLearning(molecules, silent = TRUE)

Arguments

Details

Calculate the ionization potential of a molecule. The descriptor assumes that explicit hydrogens have been added to the molecules.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named MolIP.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugIPMolecularLearning(mol)
head(dat)

Description

Descriptor that Calculates Kier and Hall Kappa Molecular Shape Indices

Usage

extractDrugKappaShapeIndices(molecules, silent = TRUE)

Arguments

Details

Kier and Hall Kappa molecular shape indices compare the molecular graph with minimal and maximal molecular graphs; see https://bit.ly/3ramdBy for details: "they are intended to capture different aspects of molecular shape. Note that hydrogens are ignored. In the following description, n denotes the number of atoms in the hydrogen suppressed graph, m is the number of bonds in the hydrogen suppressed graph. Also, let p2 denote the number of paths of length 2 and let p3 denote the number of paths of length 3".

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 3 columns named Kier1, Kier2 and Kier3:

• Kier1 - First kappa shape index

• Kier2 - Second kappa shape index

• Kier3 - Third kappa shape index

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugKappaShapeIndices(mol)
head(dat)

Description

Descriptor that Counts the Number of Occurrences of the E-State Fragments

Usage

extractDrugKierHallSmarts(molecules, silent = TRUE)

Arguments

Details

A fragment count descriptor that uses e-state fragments. Traditionally the e-state descriptors identify the relevant fragments and then evaluate the actual e-state value. However it has been shown in Butina et al. that simply using the counts of the e-state fragments can lead to QSAR models that exhibit similar performance to those built using the actual e-state indices.

Atom typing and aromaticity perception should be performed prior to calling this descriptor. The atom type definitions are taken from Hall et al. The SMARTS definitions were obtained from RDKit.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 79 columns:

References

Butina, D. , Performance of Kier-Hall E-state Descriptors in Quantitative Structure Activity Relationship (QSAR) Studies of Multifunctional Molecules, Molecules, 2004, 9:1004-1009.

Hall, L.H. and Kier, L.B. , Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information, Journal of Chemical Information and Computer Science, 1995, 35:1039-1045.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugKierHallSmarts(mol)
head(dat)

Description

Calculate the KR (Klekota and Roth) Molecular Fingerprints (in Compact Format)

Usage

extractDrugKR(molecules, silent = TRUE)

Arguments

Details

Calculate the 4860 bit fingerprint defined by Klekota and Roth.

Value

A list, each component represents one of the molecules, each element in the component represents the index of which element in the fingerprint is 1. Each component's name is the length of the fingerprints.

See Also

extractDrugKRComplete

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugKR(mol)
head(fp)

Description

Calculate the KR (Klekota and Roth) Molecular Fingerprints (in Complete Format)

Usage

extractDrugKRComplete(molecules, silent = TRUE)

Arguments

Details

Calculate the 4860 bit fingerprint defined by Klekota and Roth.

Value

An integer vector or a matrix. Each row represents one molecule, the columns represent the fingerprints.

See Also

extractDrugKR

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugKRComplete(mol)
dim(fp)

Description

Descriptor that Calculates the Number of Atoms in the Largest Chain

Usage

extractDrugLargestChain(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the number of atoms in the largest chain. Note that a chain exists if there are two or more atoms. Thus single atom molecules will return 0.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named nAtomLC.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugLargestChain(mol)
head(dat)

Description

Descriptor that Calculates the Number of Atoms in the Largest Pi Chain

Usage

extractDrugLargestPiSystem(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the number of atoms in the largest pi chain.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named nAtomP.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugLargestPiSystem(mol)
head(dat)

Description

Calculate the Ratio of Length to Breadth Descriptor

Usage

extractDrugLengthOverBreadth(molecules, silent = TRUE)

Arguments

Details

Calculates the Ratio of Length to Breadth, as a result ti does not perform any orientation and only considers the X & Y extents for a series of rotations about the Z axis (in 10 degree increments).

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns two columns named LOBMAX and LOBMIN:

• LOBMAX - The maximum L/B ratio;

• LOBMIN - The L/B ratio for the rotation that results in the minimum area (defined by the product of the X & Y extents for that orientation).

Note

The descriptor assumes that the atoms have been configured.

Examples

sdf = system.file('sysdata/OptAA3d.sdf', package = 'Rcpi')

mol = readMolFromSDF(sdf)
dat = extractDrugLengthOverBreadth(mol)
head(dat)

Description

Descriptor that Calculates the Number of Atoms in the Longest Aliphatic Chain

Usage

extractDrugLongestAliphaticChain(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the number of atoms in the longest aliphatic chain.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named nAtomLAC.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugLongestAliphaticChain(mol)
head(dat)

Description

Calculate the MACCS Molecular Fingerprints (in Compact Format)

Usage

extractDrugMACCS(molecules, silent = TRUE)

Arguments

Details

The popular 166 bit MACCS keys described by MDL.

Value

A list, each component represents one of the molecules, each element in the component represents the index of which element in the fingerprint is 1. Each component's name is the length of the fingerprints.

See Also

extractDrugMACCSComplete

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugMACCS(mol)
head(fp)

Description

Calculate the MACCS Molecular Fingerprints (in Complete Format)

Usage

extractDrugMACCSComplete(molecules, silent = TRUE)

Arguments

Details

The popular 166 bit MACCS keys described by MDL.

Value

An integer vector or a matrix. Each row represents one molecule, the columns represent the fingerprints.

See Also

extractDrugMACCS

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugMACCSComplete(mol)
dim(fp)

Description

Descriptor that Calculates the LogP Based on a Simple Equation Using the Number of Carbons and Hetero Atoms

Usage

extractDrugMannholdLogP(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the LogP based on a simple equation using the number of carbons and hetero atoms. The implemented equation was proposed in Mannhold et al.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named MLogP.

References

Mannhold, R., Poda, G. I., Ostermann, C., & Tetko, I. V. (2009). Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds. Journal of pharmaceutical sciences, 98(3), 861-893.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugMannholdLogP(mol)
head(dat)

Description

Calculate Molecular Distance Edge (MDE) Descriptors for C, N and O

Usage

extractDrugMDE(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the 10 molecular distance edge (MDE) descriptor described in Liu, S., Cao, C., & Li, Z, and in addition it calculates variants where O and N are considered.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named nAtomLAC.

References

Liu, S., Cao, C., & Li, Z. (1998). Approach to estimation and prediction for normal boiling point (NBP) of alkanes based on a novel molecular distance-edge (MDE) vector, lambda. Journal of chemical information and computer sciences, 38(3), 387-394.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugMDE(mol)
head(dat)

Description

Descriptor that Calculates the Principal Moments of Inertia and Ratios of the Principal Moments

Usage

extractDrugMomentOfInertia(molecules, silent = TRUE)

Arguments

Details

A descriptor that calculates the moment of inertia and radius of gyration. Moment of inertia (MI) values characterize the mass distribution of a molecule. Related to the MI values, ratios of the MI values along the three principal axes are also well know modeling variables. This descriptor calculates the MI values along the X, Y and Z axes as well as the ratio's X/Y, X/Z and Y/Z. Finally it also calculates the radius of gyration of the molecule.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns 7 columns named MOMI.X, MOMI.Y, MOMI.Z, MOMI.XY, MOMI.XZ, MOMI.YZ, MOMI.R:

• MOMI.X - MI along X axis

• MOMI.Y - MI along Y axis

• MOMI.Z - MI along Z axis

• MOMI.XY - X/Y

• MOMI.XZ - X/Z

• MOMI.YZ - Y/Z

• MOMI.R - Radius of gyration

One important aspect of the algorithm is that if the eigenvalues of the MI tensor are below 1e-3, then the ratio's are set to a default of 1000.

Examples

sdf = system.file('sysdata/OptAA3d.sdf', package = 'Rcpi')

mol = readMolFromSDF(sdf)
dat = extractDrugMomentOfInertia(mol)
head(dat)

Description

Calculate the FP2 Molecular Fingerprints

Usage

extractDrugOBFP2(molecules, type = c("smile", "sdf"))

Arguments

Details

Calculate the 1024 bit FP2 fingerprints provided by OpenBabel.

Value

A matrix. Each row represents one molecule, the columns represent the fingerprints.

Examples

mol1 = 'C1CCC1CC(CN(C)(C))CC(=O)CC'  # one molecule SMILE in a vector
mol2 = c('CCC', 'CCN', 'CCN(C)(C)', 'c1ccccc1Cc1ccccc1',
         'C1CCC1CC(CN(C)(C))CC(=O)CC')  # multiple SMILEs in a vector
mol3 = readChar(system.file('compseq/DB00860.sdf', package = 'Rcpi'),
                nchars = 1e+6)  # single molecule in a sdf file
mol4 = readChar(system.file('sysdata/OptAA3d.sdf', package = 'Rcpi'),
                nchars = 1e+6)  # multiple molecules in a sdf file

## Not run: 
smifp0 = extractDrugOBFP2(mol1, type = 'smile')
smifp1 = extractDrugOBFP2(mol2, type = 'smile')
sdffp0 = extractDrugOBFP2(mol3, type = 'sdf')
sdffp1 = extractDrugOBFP2(mol4, type = 'sdf')
## End(Not run)

Description

Calculate the FP3 Molecular Fingerprints

Usage

extractDrugOBFP3(molecules, type = c("smile", "sdf"))

Arguments

Details

Calculate the 64 bit FP3 fingerprints provided by OpenBabel.

Value

A matrix. Each row represents one molecule, the columns represent the fingerprints.

Examples

mol1 = 'C1CCC1CC(CN(C)(C))CC(=O)CC'  # one molecule SMILE in a vector
mol2 = c('CCC', 'CCN', 'CCN(C)(C)', 'c1ccccc1Cc1ccccc1',
         'C1CCC1CC(CN(C)(C))CC(=O)CC')  # multiple SMILEs in a vector
mol3 = readChar(system.file('compseq/DB00860.sdf', package = 'Rcpi'),
                nchars = 1e+6)  # single molecule in a sdf file
mol4 = readChar(system.file('sysdata/OptAA3d.sdf', package = 'Rcpi'),
                nchars = 1e+6)  # multiple molecules in a sdf file
## Not run: 
smifp0 = extractDrugOBFP3(mol1, type = 'smile')
smifp1 = extractDrugOBFP3(mol2, type = 'smile')
sdffp0 = extractDrugOBFP3(mol3, type = 'sdf')
sdffp1 = extractDrugOBFP3(mol4, type = 'sdf')
## End(Not run)

Description

Calculate the FP4 Molecular Fingerprints

Usage

extractDrugOBFP4(molecules, type = c("smile", "sdf"))

Arguments

Details

Calculate the 512 bit FP4 fingerprints provided by OpenBabel.

Value

A matrix. Each row represents one molecule, the columns represent the fingerprints.

Examples

mol1 = 'C1CCC1CC(CN(C)(C))CC(=O)CC'  # one molecule SMILE in a vector
mol2 = c('CCC', 'CCN', 'CCN(C)(C)', 'c1ccccc1Cc1ccccc1',
         'C1CCC1CC(CN(C)(C))CC(=O)CC')  # multiple SMILEs in a vector
mol3 = readChar(system.file('compseq/DB00860.sdf', package = 'Rcpi'),
                nchars = 1e+6)  # single molecule in a sdf file
mol4 = readChar(system.file('sysdata/OptAA3d.sdf', package = 'Rcpi'),
                nchars = 1e+6)  # multiple molecules in a sdf file

## Not run: 
smifp0 = extractDrugOBFP4(mol1, type = 'smile')
smifp1 = extractDrugOBFP4(mol2, type = 'smile')
sdffp0 = extractDrugOBFP4(mol3, type = 'sdf')
sdffp1 = extractDrugOBFP4(mol4, type = 'sdf')
## End(Not run)

Description

Calculate the MACCS Molecular Fingerprints

Usage

extractDrugOBMACCS(molecules, type = c("smile", "sdf"))

Arguments

Details

Calculate the 256 bit MACCS fingerprints provided by OpenBabel.

Value

A matrix. Each row represents one molecule, the columns represent the fingerprints.

Examples

mol1 = 'C1CCC1CC(CN(C)(C))CC(=O)CC'  # one molecule SMILE in a vector
mol2 = c('CCC', 'CCN', 'CCN(C)(C)', 'c1ccccc1Cc1ccccc1',
         'C1CCC1CC(CN(C)(C))CC(=O)CC')  # multiple SMILEs in a vector
mol3 = readChar(system.file('compseq/DB00860.sdf', package = 'Rcpi'),
                nchars = 1e+6)  # single molecule in a sdf file
mol4 = readChar(system.file('sysdata/OptAA3d.sdf', package = 'Rcpi'),
                nchars = 1e+6)  # multiple molecules in a sdf file

## Not run: 
# MACCS may not be available in current version of ChemmineOB
smifp0 = extractDrugOBMACCS(mol1, type = 'smile')
smifp1 = extractDrugOBMACCS(mol2, type = 'smile')
sdffp0 = extractDrugOBMACCS(mol3, type = 'sdf')
sdffp1 = extractDrugOBMACCS(mol4, type = 'sdf')
## End(Not run)

Description

Descriptor that Calculates the Petitjean Number of a Molecule

Usage

extractDrugPetitjeanNumber(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the Petitjean number of a molecule. According to the Petitjean definition, the eccentricity of a vertex corresponds to the distance from that vertex to the most remote vertex in the graph.

The distance is obtained from the distance matrix as the count of edges between the two vertices. If r(i) is the largest matrix entry in row i of the distance matrix D, then the radius is defined as the smallest of the r(i). The graph diameter D is defined as the largest vertex eccentricity in the graph. (http://www.edusoft-lc.com/molconn/manuals/400/chaptwo.html)

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named PetitjeanNumber.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugPetitjeanNumber(mol)
head(dat)

Description

Descriptor that Calculates the Petitjean Shape Indices

Usage

extractDrugPetitjeanShapeIndex(molecules, silent = TRUE)

Arguments

Details

The topological and geometric shape indices described Petitjean and Bath et al. respectively. Both measure the anisotropy in a molecule.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns two columns named topoShape (Topological Shape Index) and geomShape (Geometric Shape Index).

References

Petitjean, M., Applications of the radius-diameter diagram to the classification of topological and geometrical shapes of chemical compounds, Journal of Chemical Information and Computer Science, 1992, 32:331-337

Bath, P.A. and Poirette, A.R. and Willet, P. and Allen, F.H. , The Extent of the Relationship between the Graph-Theoretical and the Geometrical Shape Coefficients of Chemical Compounds, Journal of Chemical Information and Computer Science, 1995, 35:714-716.

Examples

sdf = system.file('sysdata/OptAA3d.sdf', package = 'Rcpi')

mol = readMolFromSDF(sdf)
dat = extractDrugPetitjeanShapeIndex(mol)
head(dat)

Description

Calculate the PubChem Molecular Fingerprints (in Compact Format)

Usage

extractDrugPubChem(molecules, silent = TRUE)

Arguments

Details

Calculate the 881 bit fingerprints defined by PubChem.

Value

A list, each component represents one of the molecules, each element in the component represents the index of which element in the fingerprint is 1. Each component's name is the length of the fingerprints.

See Also

extractDrugPubChemComplete

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugPubChem(mol)
head(fp)

Description

Calculate the PubChem Molecular Fingerprints (in Complete Format)

Usage

extractDrugPubChemComplete(molecules, silent = TRUE)

Arguments

Details

Calculate the 881 bit fingerprints defined by PubChem.

Value

An integer vector or a matrix. Each row represents one molecule, the columns represent the fingerprints.

See Also

extractDrugPubChem

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugPubChemComplete(mol)
dim(fp)

Description

Descriptor that Calculates the Number of Nonrotatable Bonds on A Molecule

Usage

extractDrugRotatableBondsCount(molecules, silent = TRUE)

Arguments

Details

The number of rotatable bonds is given by the SMARTS specified by Daylight on SMARTS tutorial (https://www.daylight.com/dayhtml_tutorials/languages/smarts/smarts_examples.html)

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named nRotB.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugRotatableBondsCount(mol)
head(dat)

Description

Descriptor that Calculates the Number Failures of the Lipinski's Rule Of Five

Usage

extractDrugRuleOfFive(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the number failures of the Lipinski's Rule Of Five: http://en.wikipedia.org/wiki/Lipinski%27s_Rule_of_Five.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named LipinskiFailures.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugRuleOfFive(mol)
head(dat)

Description

Calculate the Shortest Path Molecular Fingerprints (in Compact Format)

Usage

extractDrugShortestPath(molecules, depth = 6, size = 1024, silent = TRUE)

Arguments

Details

Calculate the fingerprint based on the shortest paths between pairs of atoms and takes into account ring systems, charges etc.

Value

A list, each component represents one of the molecules, each element in the component represents the index of which element in the fingerprint is 1. Each component's name is the length of the fingerprints.

See Also

extractDrugShortestPathComplete

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugShortestPath(mol)
head(fp)

Description

Calculate the Shortest Path Molecular Fingerprints (in Complete Format)

Usage

extractDrugShortestPathComplete(
  molecules,
  depth = 6,
  size = 1024,
  silent = TRUE
)

Arguments

Details

Calculate the fingerprint based on the shortest paths between pairs of atoms and takes into account ring systems, charges etc.

Value

An integer vector or a matrix. Each row represents one molecule, the columns represent the fingerprints.

See Also

extractDrugShortestPath

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugShortestPathComplete(mol)
dim(fp)

Description

Calculate the Standard Molecular Fingerprints (in Compact Format)

Usage

extractDrugStandard(molecules, depth = 6, size = 1024, silent = TRUE)

Arguments

Details

Calculate the standard molecular fingerprints. Considers paths of a given length. This is hashed fingerprints, with a default length of 1024.

Value

A list, each component represents one of the molecules, each element in the component represents the index of which element in the fingerprint is 1. Each component's name is the length of the fingerprints.

See Also

extractDrugStandardComplete

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugStandard(mol)
head(fp)

Description

Calculate the Standard Molecular Fingerprints (in Complete Format)

Usage

extractDrugStandardComplete(molecules, depth = 6, size = 1024, silent = TRUE)

Arguments

Details

Calculate the standard molecular fingerprints. Considers paths of a given length. This is hashed fingerprints, with a default length of 1024.

Value

An integer vector or a matrix. Each row represents one molecule, the columns represent the fingerprints.

See Also

extractDrugStandard

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
fp  = extractDrugStandardComplete(mol)
dim(fp)

Description

Descriptor of Topological Polar Surface Area Based on Fragment Contributions (TPSA)

Usage

extractDrugTPSA(molecules, silent = TRUE)

Arguments

Details

Calculate the descriptor of topological polar surface area based on fragment contributions (TPSA).

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named TopoPSA.

References

Ertl, P., Rohde, B., & Selzer, P. (2000). Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. Journal of medicinal chemistry, 43(20), 3714-3717.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugTPSA(mol)
head(dat)

Description

Descriptor that Calculates the Volume of A Molecule

Usage

extractDrugVABC(molecules, silent = TRUE)

Arguments

Details

This descriptor calculates the volume of a molecule.

Value

A data frame, each row represents one of the molecules, each column represents one feature. This function returns one column named VABC.

Examples

smi = system.file('vignettedata/FDAMDD.smi', package = 'Rcpi')

mol = readMolFromSmi(smi, type = 'mol')
dat = extractDrugVABC(mol)
head(dat)

Description

Descriptor that Calculates the Vertex Adjacency Information of A Molecule

Usage

extractDrugVAdjMa(molecules, silent = TRUE)

Arguments

Details

Vertex adjacency information (magnitude): $1 + \log_2^m$ where $m$ is the number of heavy-heavy bonds. If $m$ is zero, then 0 is returned.