title: PyDPI: freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies.
abstract: The rapidly increasing amount of publicly available data in biology and chemistry enables researchers to revisit interaction problems by systematic integration and analysis of heterogeneous data. Herein, we developed a comprehensive python package to emphasize the integration of chemoinformatics and bioinformatics into a molecular informatics platform for drug discovery. PyDPI (drug-protein interaction with Python) is a powerful python toolkit for computing commonly used structural and physicochemical features of proteins and peptides from amino acid sequences, molecular descriptors of drug molecules from their topology, and protein-protein interaction and protein-ligand interaction descriptors. It computes 6 protein feature groups composed of 14 features that include 52 descriptor types and 9890 descriptors, 9 drug feature groups composed of 13 descriptor types that include 615 descriptors. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pair fingerprints, topological torsion fingerprints, and Morgan/circular fingerprints. By combining different types of descriptors from drugs and proteins in different ways, interaction descriptors representing protein-protein or drug-protein interactions could be conveniently generated. These computed descriptors can be widely used in various fields relevant to chemoinformatics, bioinformatics, and chemogenomics. PyDPI is freely available via https://sourceforge.net/projects/pydpicao/.
authors.affiliation: School of Pharmaceutical Sciences, Central South University , Changsha 410013, P.R. China.
authors.forename: Dong-Sheng
authors.initials: DS
authors.lastname: Cao
authors.forename: Yi-Zeng
authors.initials: YZ
authors.lastname: Liang
authors.forename: Jun
authors.initials: J
authors.lastname: Yan
authors.forename: Gui-Shan
authors.initials: GS
authors.lastname: Tan
authors.forename: Qing-Song
authors.initials: QS
authors.lastname: Xu
authors.forename: Shao
authors.initials: S
authors.lastname: Liu
mesh_terms: D001665:Binding Sites; D001688:Biological Products / Q000737:chemistry*; D019295:Computational Biology / Q000706:statistics & numerical data*; D062126:Databases, Chemical; D062313:Databases, Pharmaceutical; D015195:Drug Design; D055808:Drug Discovery; D015507:Drugs, Investigational / Q000737:chemistry*; D006801:Humans; D008024:Ligands; D055553:Prescription Drugs / Q000737:chemistry*; D011485:Protein Binding; D011506:Proteins / Q000819:agonists / Q000037:antagonists & inhibitors / Q000737:chemistry*; D012107:Research Design; D012984:Software
chemical_list: D001688:Biological Products; D015507:Drugs, Investigational; D008024:Ligands; D055553:Prescription Drugs; D011506:Proteins