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Toolkit for Compound-Protein Interaction in Drug Discovery
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README

# 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>
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