Molecular informatics toolkit with a comprehensive integration of bioinformatics and cheminformatics tools for drug discovery.
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README.md

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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 Alex F. Chen.},
  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},
  issn = {1367-4803},
  url = {http://bioinformatics.oxfordjournals.org/content/31/2/279}
}

Installation

To install the Rcpi package:

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:

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.