Classification of pharmacological activity of drugs using support vector machine

Authors:
Yoshimasa Takahashi;Katsumi Nishikoori;Satoshi Fujishima
Affiliations:
Department of Knowledge-based Information Engineering, Toyohashi University of Technology, Toyohashi, Japan;Department of Knowledge-based Information Engineering, Toyohashi University of Technology, Toyohashi, Japan;Department of Knowledge-based Information Engineering, Toyohashi University of Technology, Toyohashi, Japan
Venue:
AM'03 Proceedings of the Second international conference on Active Mining
Year:
2003

Citing 2
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Abstract

In the present work, we investigated an applicability of Support Vector Machine (SVM) to classify of pharmacological activities of drugs. The numerical description of each drug's chemical structure was based on the Topological Fragment Spectra (TFS) proposed by the authors. 1,227 Dopamine antagonists that interact with different types of receptors (D1, D2, D3 and D4) were used for training SVM. For a prediction set of 137 drugs not included in the training set, the obtained SVM classified 123 (89.8 %) drugs into their own activity classes correctly. The comparison between using SVM and artificial neural network will also be discussed.