Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents

Statistical-learning methods have been developed for facilitating the prediction of pharmacokinetic and
toxicological properties of chemical agents. These methods employ a variety of molecular descriptors to
characterize structural and physicochemical properties of molecules. Some of these descriptors are specifically
designed for the study of a particular type of properties or agents, and their use for other properties or
agents might generate noise and affect the prediction accuracy of a statistical learning system. This work
examines to what extent the reduction of this noise can improve the prediction accuracy of a statistical
learning system. A feature selection method, recursive feature elimination (RFE), is used to automatically
select molecular descriptors for support vector machines (SVM) prediction of P-glycoprotein substrates
(P-gp), human intestinal absorption of molecules (HIA), and agents that cause torsades de pointes (TdP), a
rare but serious side effect. RFE significantly reduces the number of descriptors for each of these properties
thereby increasing the computational speed for their classification. The SVM prediction accuracies of P-gp
and HIA are substantially increased and that of TdP remains unchanged by RFE. These prediction accuracies
are comparable to those of earlier studies derived from a selective set of descriptors. Our study suggests
that molecular feature selection is useful for improving the speed and, in some cases, the accuracy of statistical
learning methods for the prediction of pharmacokinetic and toxicological properties of chemical agents.