Prediction of drug activity using molecular fragments-based representation and RFE support vector machine algorithm

Authors:
Gonzalo Cerruela García;Irene Luque Ruiz;Miguel Ángel Gómez-Nieto
Affiliations:
University of Córdoba, Department of Computing and Numerical Analysis, Córdoba, Spain;University of Córdoba, Department of Computing and Numerical Analysis, Córdoba, Spain;University of Córdoba, Department of Computing and Numerical Analysis, Córdoba, Spain
Venue:
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Year:
2011

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Abstract

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.