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- Title
QSAR and classification models of a novel series of COX-2 selective inhibitors: 1, 5-diarylimidazoles based on support vector machines.
- Authors
Liu, H. X.; Zhang, R. S.; Yao, X. J.; Liu, M. C.; Hu, Z. D.; Fan, B. T.
- Abstract
The support vector machine, which is a novel algorithm from the machine learning community, was used to develop quantitation and classification models which can be used as a potential screening mechanism for a novel series of COX-2 selective inhibitors. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum-chemical features. The Heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modelling results in a nonlinear, seven-descriptor model based on SVMs with root mean-square errors of 0.107 and 0.136 for training and prediction sets, respectively. The best classification results are found using SVMs: the accuracy for training and test sets is 91.2% and 88.2%, respectively. This paper proposes a new and effective method for drug design and screening.
- Subjects
QSAR models; CYCLOOXYGENASE 2; HEURISTIC programming; STRUCTURE-activity relationships; ALGORITHMS; DRUG design
- Publication
Journal of Computer-Aided Molecular Design, 2004, Vol 18, Issue 6, p389
- ISSN
0920-654X
- Publication type
Article
- DOI
10.1007/s10822-004-2722-1