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

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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.

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Authors and Affiliations

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

    H.X. Liu, R.S. Zhang, R.S. Zhang, X.J. Yao, M.C. Liu & Z.D. Hu

  2. Université Paris 7-Denis Diderot, ITODYS 1, Rue Guy de la Brosse, F-75005, Paris, France

    B.T. Fan

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  1. H.X. Liu
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  2. R.S. Zhang
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Liu, H., Zhang, R., Yao, X. et al. QSAR and classification models of a novel series of COX-2 selective inhibitors: 1, 5-diarylimidazoles based on support vector machines. J Comput Aided Mol Des 18, 389–399 (2004). https://doi.org/10.1007/s10822-004-2722-1

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