PyDPI: freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies

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...

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Vydáno v:Journal of chemical information and modeling Ročník 53; číslo 11; s. 3086
Hlavní autoři: Cao, Dong-Sheng, Liang, Yi-Zeng, Yan, Jun, Tan, Gui-Shan, Xu, Qing-Song, Liu, Shao
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 25.11.2013
Témata:
Binding Sites
Biological Products - chemistry
Computational Biology - statistics & numerical data
Databases, Chemical
Databases, Pharmaceutical
Drug Design
Drug Discovery
Drugs, Investigational - chemistry
Humans
Ligands
Prescription Drugs - chemistry
Protein Binding
Proteins - agonists
Proteins - antagonists & inhibitors
Proteins - chemistry
Research Design
Software
ISSN:1549-960X, 1549-960X
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Popis
Shrnutí: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/.
Bibliografie:ObjectType-Article-1
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ISSN:1549-960X
1549-960X
DOI:10.1021/ci400127q