Package: DeepPINCS 1.13.0

Dongmin Jung

DeepPINCS: Protein Interactions and Networks with Compounds based on Sequences using Deep Learning

The identification of novel compound-protein interaction (CPI) is important in drug discovery. Revealing unknown compound-protein interactions is useful to design a new drug for a target protein by screening candidate compounds. The accurate CPI prediction assists in effective drug discovery process. To identify potential CPI effectively, prediction methods based on machine learning and deep learning have been developed. Data for sequences are provided as discrete symbolic data. In the data, compounds are represented as SMILES (simplified molecular-input line-entry system) strings and proteins are sequences in which the characters are amino acids. The outcome is defined as a variable that indicates how strong two molecules interact with each other or whether there is an interaction between them. In this package, a deep-learning based model that takes only sequence information of both compounds and proteins as input and the outcome as output is used to predict CPI. The model is implemented by using compound and protein encoders with useful features. The CPI model also supports other modeling tasks, including protein-protein interaction (PPI), chemical-chemical interaction (CCI), or single compounds and proteins. Although the model is designed for proteins, DNA and RNA can be used if they are represented as sequences.

Authors:Dongmin Jung [cre, aut]

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NEWS

# Install 'DeepPINCS' in R:

install.packages('DeepPINCS', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openjdk– OpenJDK Java runtime, using Hotspot JIT

Datasets:

SARS_CoV2_3CL_Protease - Amino Acid Sequence for the SARS coronavirus 3C-like Protease
antiviral_drug - List of antiviral drugs with SMILES strings
example_bioassay - Example Data for PubChem AID1706 bioassay
example_cci - Example Data for Chemical-Chemical Interactions
example_chem - Example Data for Compounds
example_cpi - Example Data for Compound-Protein Interactions
example_pd - Example Data for Primer-Dimer
example_ppi - Example Data for Protein-Protein Interactions
example_prot - Example Data for Proteins

On BioConductor:DeepPINCS-1.13.0(bioc 3.20)DeepPINCS-1.12.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

15 exports 1.31 score 137 dependencies 2 dependents

Last updated 2 months agofrom:ff29da09e4

Exports:cnn_in_out fit_cpi gcn_in_out get_canonical_smiles get_fingerprint get_graph_structure_node_feature get_seq_encode_pad metric_concordance_index metric_f1_score mlp_in_out multiple_sampling_generator predict_cpi rnn_in_out seq_check seq_preprocessing

Dependencies:askpass backports base64enc BH bit bit64 bslib cachem CatEncoders cli clipr colorspace config cpp11 crayon curl data.table data.tree DiagrammeR digest dplyr dtt english evaluate fansi farver fastmap fastmatch fingerprint float fontawesome fs generics glue here highr hms htmltools htmlwidgets httr hunspell igraph ISOcodes iterators itertools jquerylib jsonlite keras knitr koRpus koRpus.lang.en labeling lattice lexicon lgr lifecycle magrittr matlab Matrix MatrixExtra memoise mgsub mime mlapi munsell NLP openssl pillar pkgconfig png prettyunits processx progress PRROC ps purrr qdapRegex quanteda R6 rappdirs rcdk rcdklibs RColorBrewer Rcpp RcppArmadillo RcppTOML readr reticulate RhpcBLASctl rJava rlang rmarkdown rprojroot rsparse rstudioapi rvest sass scales selectr slam SnowballC stopwords stringdist stringi stringr sylly sylly.en sys syuzhet tensorflow text2vec textclean textshape textstem tfautograph tfruns tibble tidyr tidyselect tinytex tm tokenizers ttgsea tzdb utf8 vctrs viridisLite visNetwork vroom webchem whisker withr xfun xml2 yaml zeallot zoo

Protein Interactions and Networks with Compounds based on Sequences using Deep Learning

Dongmin Jung

Rendered fromDeepPINCS.Rmdusingknitr::rmarkdownon Jun 17 2024.

Last update: 2021-09-30
Started: 2021-03-21

Help page	Topics
List of antiviral drugs with SMILES strings	antiviral_drug
Deep learning model fitting and prediction for compound-protein interactions	fit_cpi predict_cpi
Input and output tensors of encoders	cnn_in_out gcn_in_out mlp_in_out rnn_in_out
Example Data for PubChem AID1706 bioassay	example_bioassay
Example Data for Chemical-Chemical Interactions	example_cci
Example Data for Compounds	example_chem
Example Data for Compound-Protein Interactions	example_cpi
Example Data for Primer-Dimer	example_pd
Example Data for Protein-Protein Interactions	example_ppi
Example Data for Proteins	example_prot
Convert SMILES strings to canonical SMILES strings	get_canonical_smiles
Molecular fingerprint of compounds from SMILES strings	get_fingerprint
Graph structure and node features from SMILES strings	get_graph_structure_node_feature
Vectorization of characters of strings	get_seq_encode_pad
Concordance index	metric_concordance_index
F1-score	metric_f1_score
Generator function for multiple inputs	multiple_sampling_generator
Amino Acid Sequence for the SARS coronavirus 3C-like Protease	SARS_CoV2_3CL_Protease
Check SMILES strings and amino acid sequences	seq_check
Preprocessing for SMILES strings and amino acid sequences	seq_preprocessing

Package: DeepPINCS 1.13.0

DeepPINCS: Protein Interactions and Networks with Compounds based on Sequences using Deep Learning

Protein Interactions and Networks with Compounds based on Sequences using Deep Learning

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