Molecules 2012, 17(4), 4560-4582; doi:10.3390/molecules17044560
A Support Vector Machine Classification Model for Benzo[c]phenathridine Analogues with Topoisomerase-I Inhibitory Activity
Department of Medicinal Chemistry, School of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, 41 Dinh Tien Hoang St., District 1, Ho Chi Minh City, Vietnam
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Received: 13 March 2012 / Revised: 8 April 2012 / Accepted: 10 April 2012 / Published: 17 April 2012
(This article belongs to the Section Medicinal Chemistry)
Abstract
Benzo[c]phenanthridine (BCP) derivatives were identified as topoisomerase I (TOP-I) targeting agents with pronounced antitumor activity. In this study, a support vector machine model was performed on a series of 73 analogues to classify BCP derivatives according to TOP-I inhibitory activity. The best SVM model with total accuracy of 93% for training set was achieved using a set of 7 descriptors identified from a large set via a random forest algorithm. Overall accuracy of up to 87% and a Matthews coefficient correlation (MCC) of 0.71 were obtained after this SVM classifier was validated internally by a test set of 15 compounds. For two external test sets, 89% and 80% BCP compounds, respectively, were correctly predicted. The results indicated that our SVM model could be used as the filter for designing new BCP compounds with higher TOP-I inhibitory activity. View Full-TextKeywords:
support vector machine; SVM; classification; topoisomerase; anticancer; benzo[c]phenanthridine; drug design; pharmacoinformatics
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Thai, K.-M.; Nguyen, T.-Q.; Ngo, T.-D.; Tran, T.-D.; Huynh, T.-N.-P. A Support Vector Machine Classification Model for Benzo[c]phenathridine Analogues with Topoisomerase-I Inhibitory Activity. Molecules 2012, 17, 4560-4582.
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