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ADMET prediction of drug compounds
(1) Prediction of aqueous solubility of druglike organic compounds using partial least squares, back-propagation network and support vector machine
Aqueous solubility of drug compounds plays a very important role in drug research and development. In this study, three commonly used methods, namely partial least squares (PLS), back-propagation network (BPN) and support vector regression (SVR), were developed to model quantitative structure-property relationship (QSPR) for the aqueous solubility of 180 druglike compounds. 28 molecular descriptors were used to relate the drug aqueous solubility. It is shown that three models can provide good predictive ability of drug aqueous solubility. The predictive ability of SVR was found to be superior to ones of PLS and BP for 180 druglike compounds. The best SVR model established, had an overall R2 o
