PyDPI: Freely Available Python Package for Chemoinformatics, Bioinformatics, and Chemogenomics Studies

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• 作者：Dong-Sheng Cao ; Yi-Zeng Liang ; Jun Yan ; Gui-Shan Tan ; Qing-Song Xu ; Shao Liu
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2013
• 出版时间：November 25, 2013
• 年：2013
• 卷：53
• 期：11
• 页码：3086-3096
• 全文大小：320K
• 年卷期：v.53,no.11(November 25, 2013)
• ISSN：1549-960X

文摘

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鈥損rotein 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鈥損rotein interaction and protein鈥搇igand 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鈥損rotein or drug鈥損rotein 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 cao/" class="extLink">https://sourceforge.net/projects/pydpicao/.