# Rcpi ## Introduction The Rcpi package offers an R/Bioconductor package emphasizing the comprehensive integration of bioinformatics and chemoinformatics into a molecular informatics platform for drug discovery. See `vignette('Rcpi')` for the comprehensive user guide. ## Installation To install the Rcpi package in R, simply type source('http://bioconductor.org/biocLite.R') biocLite('Rcpi') To make the Rcpi package fully functional (especially the Open Babel related functionalities), we recommend the users also install the _Enhances_ packages by using: source('http://bioconductor.org/biocLite.R') biocLite('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. ## 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, chemoinformatics, proteochemometrics and chemogenomics. ## Links * Bioconductor Page: http://bioconductor.org/packages/release/bioc/html/Rcpi.html * Track Devel: https://github.com/road2stat/Rcpi * Report Bugs: https://github.com/road2stat/Rcpi/issues ## Contact The Rcpi package is developed by Computational Biology and Drug Design Group, Central South University, China. * Nan Xiao <road2stat@gmail.com> * Dongsheng Cao <oriental-cds@163.com> * Qingsong Xu <dasongxu@gmail.com>