ChemoPy: freely available python']python package for computational biology and chemoinformatics

被引:170
作者
Cao, Dong-Sheng [1 ]
Xu, Qing-Song [2 ]
Hu, Qian-Nan [3 ]
Liang, Yi-Zeng [1 ]
机构
[1] Cent S Univ, Res Ctr Modernizat Tradit Chinese Medicines, Changsha 410083, Hunan, Peoples R China
[2] Cent S Univ, Sch Math & Stat, Changsha 410083, Hunan, Peoples R China
[3] Wuhan Univ, Minist Educ, Key Lab Combinatorial Biosynth & Drug Discovery, Wuhan 430071, Peoples R China
基金
中国国家自然科学基金;
关键词
AQUEOUS SOLUBILITY; RANDOM FOREST; DRUG DESIGN; FB-QSAR; PREDICTION; PROTEIN; INHIBITORS; NETWORK; CLASSIFICATION; CHEMISTRY;
D O I
10.1093/bioinformatics/btt105
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: Molecular representation for small molecules has been routinely used in QSAR/SAR, virtual screening, database search, ranking, drug ADME/T prediction and other drug discovery processes. To facilitate extensive studies of drug molecules, we developed a freely available, open-source python package called chemoinformatics in python (ChemoPy) for calculating the commonly used structural and physicochemical features. It computes 16 drug feature groups composed of 19 descriptors that include 1135 descriptor values. 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 pairs fingerprints, topological torsion fingerprints and Morgan/circular fingerprints. By applying a semi-empirical quantum chemistry program MOPAC, ChemoPy can also compute a large number of 3D molecular descriptors conveniently.
引用
收藏
页码:1092 / 1094
页数:3
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