Prediction of Drug Activity Using Molecular Fragments-Based Representation and RFE Support Vector Machine Algorithm

This paper describes the use of a support vector machine algorithm for the classification of molecules database in order for the prediction of the activity of drugs. Molecules database are fragmented, and each molecule is represented by a set of contained fragments. Molecular weighted descriptors are tested for the representation of molecular fragments in order to represent the dataset as a MxF array where each element takes the value of the molecular weighted descriptor calculated for the fragment. As weighted descriptors take into account distances and heteroatoms present in the fragments, the representation space allows the discrimination of similar structural fragments. A Support Vector Machine algorithm is used for the classification process for a training set. Prediction of the activity of the test set is carried out in function of results of training stage and the application of a proposed heuristic. Results obtained shows that the use of weighted molecular descriptors improves the prediction of drug activity for heterogeneous datasets.