MODEL - Molecular descriptor lab: A web-based server for computing structural and physicochemical features of compounds

被引:52
作者
Li, Z. R.
Han, L. Y.
Xue, Y.
Yap, C. W.
Li, H.
Jiang, L.
Chen, Y. Z.
机构
[1] Natl Univ Singapore, Dept Computat Sci, Bioinformat & Drug Design Grp, Singapore 117543, Singapore
[2] Sichuan Univ, Coll Chem, Chengdu 610064, Peoples R China
[3] Shanghai Ctr Bioinformat Technol, Shanghai, Peoples R China
关键词
statistical learning methods; pharmacodynamic; pharmacokinetic; toxicology; QSAR; QSPR; molecular descriptors; structural diversity;
D O I
10.1002/bit.21214
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Molecular descriptors represent structural and physicochemical features of compounds. They have been extensively used for developing statistical models, such as quantitative structure activity relationship (QSAR) and artificial neural networks (NN), for computer prediction of the pharmacodynamic, pharmacokinetic, or toxicological properties of compounds from their structure. While computer programs have been developed for computing molecular descriptors, there is a lack of a freely accessible one. We have developed a web-based server, MODEL (Molecular Descriptor Lab), for computing a comprehensive set of 3,778 molecular descriptors, which is significantly more than the similar to 1,600 molecular descriptors computed by other software. Our computational algorithms have been extensively tested and the computed molecular descriptors have been used in a number of published works of statistical models for predicting variety of pharmacodynamic, pharmacokinetic, and toxicological properties of compounds. Several testing studies on the computed molecular descriptors are discussed. MODEL is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/model/model.cgi free of charge for academic use.
引用
收藏
页码:389 / 396
页数:8
相关论文
共 48 条
[1]   Designing libraries with CNS activity [J].
Ajay ;
Bemis, GW ;
Murcko, MA .
JOURNAL OF MEDICINAL CHEMISTRY, 1999, 42 (24) :4942-4951
[2]   Predicting protein-protein interactions from primary structure [J].
Bock, JR ;
Gough, DA .
BIOINFORMATICS, 2001, 17 (05) :455-460
[3]   Drug design by machine learning: support vector machines for pharmaceutical data analysis [J].
Burbidge, R ;
Trotter, M ;
Buxton, B ;
Holden, S .
COMPUTERS & CHEMISTRY, 2001, 26 (01) :5-14
[4]   QSAR and molecular modeling studies of baclofen analogues as GABAB agonists.: Insights into the role of the aromatic moiety in GABAB binding and activation [J].
Costantino, G ;
Macchiarulo, A ;
Guadix, AE ;
Pellicciari, R .
JOURNAL OF MEDICINAL CHEMISTRY, 2001, 44 (11) :1827-1832
[5]   Predicting blood-brain barrier permeation from three-dimensional molecular structure [J].
Crivori, P ;
Cruciani, G ;
Carrupt, PA ;
Testa, B .
JOURNAL OF MEDICINAL CHEMISTRY, 2000, 43 (11) :2204-2216
[6]   VolSurf: a new tool for the pharmacokinetic optimization of lead compounds [J].
Cruciani, G ;
Pastor, M ;
Guba, W .
EUROPEAN JOURNAL OF PHARMACEUTICAL SCIENCES, 2000, 11 :S29-S39
[7]   Feature subset selection for splice site prediction [J].
Degroeve, S ;
De Baets, B ;
Van de Peer, Y ;
Rouzé, P .
BIOINFORMATICS, 2002, 18 :S75-S83
[8]   Predicting protein-ligand binding affinities using novel geometrical descriptors and machine-learning methods [J].
Deng, W ;
Breneman, C ;
Embrechts, MJ .
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 2004, 44 (02) :699-703
[9]   Predicting CNS permeability of drug molecules: comparison of neural network and support vector machine algorithms [J].
Doniger, S ;
Hofmann, T ;
Yeh, J .
JOURNAL OF COMPUTATIONAL BIOLOGY, 2002, 9 (06) :849-864
[10]   Drug discovery: A historical perspective [J].
Drews, J .
SCIENCE, 2000, 287 (5460) :1960-1964