QSPR models for prediction of bovine serum albumin-water partition coefficients of organic compounds and drugs based on enhanced replacement method and support vector machine

A quantitative structure property relationship (QSPR) study based on enhanced replacement method (ERM) and support vector machine (SVM) was used to correlate molecular structures to their bovine serum albumin water partition coefficients (KBSA/W). A wide variety of natural organic compounds and drugs were selected as a dataset and suitable sets of molecular descriptors were calculated using Dragon package. ERM was used as variable selection method. The nonlinear-support vector machine models were applied to correlate the ERM-selected molecular descriptors with the experimental values of KBSA/W. Results obtained demonstrate the reliability and good predictability of support vector machine model to predict KBSA/W of organic compounds and drugs. Satisfactory results demonstrate that the ERM approach is a very powerful method for variable selection and the predictive ability of the SVM model is superior to those acquired by ERM.