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# Rcpi <img src="man/figures/logo.png" align="right" width="120" /> <!-- badges: start --> [![bioc](]( [![downloads](]( [![R-CMD-check](]( <!-- badges: end --> ## Overview Rcpi offers a molecular informatics toolkit with a comprehensive integration of bioinformatics and cheminformatics tools for drug discovery. For more information, please see our paper <[DOI:10.1093/bioinformatics/btu624](> ([PDF]( ## Paper Citation Formatted citation: Dong-Sheng Cao, Nan Xiao, Qing-Song Xu, and Alex F. Chen. (2015). Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions. _Bioinformatics_ 31 (2), 279-281. BibTeX entry: ``` @article{Rcpi2015, author = {Cao, Dong-Sheng and Xiao, Nan and Xu, Qing-Song and Chen, Alex F.}, title = {{Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions}}, journal = {Bioinformatics}, year = {2015}, volume = {31}, number = {2}, pages = {279--281}, doi = {10.1093/bioinformatics/btu624} } ``` ## Installation To install the `Rcpi` package: ```r install.packages("BiocManager") BiocManager::install("Rcpi") ``` To make the package fully functional (especially the Open Babel related functions), we recommend installing the _Enhances_ packages by: ```r BiocManager::install("Rcpi", dependencies = c("Imports", "Enhances")) ``` Several dependencies of the Rcpi package may require some system-level libraries, check the corresponding manuals of these packages for detailed installation guides. Browse the package vignettes: [[1](], [[2](] for a quick-start. ## Features Rcpi implemented and integrated the state-of-the-art protein sequence descriptors and molecular descriptors/fingerprints with R. For protein sequences, the Rcpi package could - Calculate six protein descriptor groups composed of fourteen types of commonly used structural and physicochemical descriptors that include 9920 descriptors. - Calculate six types of generalized scales-based descriptors derived by various dimensionality reduction methods for proteochemometric (PCM) modeling. - Parallellized pairwise similarity computation derived by protein sequence alignment and Gene Ontology (GO) semantic similarity measures within a list of proteins. For small molecules, the Rcpi package could - Calculate 307 molecular descriptors (2D/3D), including constitutional, topological, geometrical, and electronic descriptors, etc. - Calculate more than ten types of molecular fingerprints, including FP4 keys, E-state fingerprints, MACCS keys, etc., and parallelized chemical similarity search. - Parallelized pairwise similarity computation derived by fingerprints and maximum common substructure search within a list of small molecules. By combining various types of descriptors for drugs and proteins in different methods, interaction descriptors representing protein-protein or compound-protein interactions could be conveniently generated with Rcpi, including: - Two types of compound-protein interaction (CPI) descriptors - Three types of protein-protein interaction (PPI) descriptors Several useful auxiliary utilities are also shipped with Rcpi: - Parallelized molecule and protein sequence retrieval from several online databases, like PubChem, ChEMBL, KEGG, DrugBank, UniProt, RCSB PDB, etc. - Loading molecules stored in SMILES/SDF files and loading protein sequences from FASTA/PDB files - Molecular file format conversion The computed protein sequence descriptors, molecular descriptors/fingerprints, interaction descriptors and pairwise similarities are widely used in various research fields relevant to drug disvery, primarily bioinformatics, cheminformatics, proteochemometrics, and chemogenomics. ## Contribute To contribute to this project, please take a look at the [Contributing Guidelines]( first. Please note that this project is released with a [Contributor Code of Conduct]( By participating in this project you agree to abide by its terms.