Abstract
Support vector machine (SVM) was used to develop a nonlinear quantitative structure–activity relationship (QSAR) model for the prediction of the activities of the adenosine A2A receptor antagonists. Six molecular descriptors selected by the heuristic method (HM) in CODESSA were used as inputs for SVM. The results obtained by SVM were compared with those obtained by HM. The mean squared errors (MSEs) for the training set given by HM and SVM are 0.08 and 0.05, respectively, which shows the performance of SVM model is better than that of the HM model.
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Acknowledgments
This study is supported by National Natural Science Foundation of P.R.China (No. 90912003, 60773108, and 90812001). The authors also thank the R Development Core Team for affording the free R2.4.1 software.
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Lu, P., Wei, X., Zhang, R. et al. Prediction of the binding affinities of adenosine A2A receptor antagonists based on the heuristic method and support vector machine. Med Chem Res 20, 1220–1228 (2011). https://doi.org/10.1007/s00044-010-9431-1
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DOI: https://doi.org/10.1007/s00044-010-9431-1