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J Comput Aided Mol Des. 2004 Jun;18(6):389-99.

QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines.

Author information

1
Department of Chemistry, Lanzhou University, Lanzhou 730000, China.

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.

PMID:
15663000
[Indexed for MEDLINE]

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