Prediction of pKa for Neutral and Basic Drugs Based on Radial Basis Function Neural Networks and the Heuristic Method
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Quantitative structure–property relationships (QSPR) were developed to predict the pKa values of a set of neutral and basic drugs via linear and nonlinear methods. The ability of the models to predict pKa was assessed and compared.
The descriptors of 74 neutral and basic drugs in this study were calculated by the software CODESSA, which can calculate constitutional, topological, geometrical, electrostatic, and quantum chemical descriptors. Linear and nonlinear QSPR models were developed based on the heuristic method (HM) and radial basis function neural networks (RBFNN), respectively. The heuristic method was also used for the preselection of appropriate molecular descriptors.
The obtained linear model had a correlation coefficient of r = 0.884, F = 37.72 with a root-mean-squared (RMS) error of 0.482 for the training set, and r = 0.693, F = 11.99, and RMS = 0.987 for the test set. The RMS in predicting the overall data set is 0.619. The nonlinear model gave better results; for the training set, r = 0.886, F = 202.314, and RMS = 0.458, and for the test set r = 0.737, F = 15.41, and RMS = 0.613. The RMS error in prediction for overall data set is 0.493. Prediction results from nonlinear model are in good agreement with experimental values.
In present study, we developed a QSPR model to predict the important parameter (pKa) of neutral and basic drugs. The model is useful in predicting pKa during the discovery of new drugs when experimental data are unknown.
Key Wordsneutral and basic drugs quantitative structure–property relationship radial basis function neural networks the heuristic method
The authors thank the National Natural Science Foundation of China (NSFC) Fund (NO.20305008) for supporting this project.
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