A Support Vector Machine Classification Model for Benzo[c]phenathridine Analogues with Topoisomerase-I Inhibitory Activity

Molecules

Khac-Minh Thai¹  Thuy-Quyen Nguyen²  Trieu-Du Ngo²  Thanh-Dao Tran² 

[1] 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;

关键词: support vector machine;    SVM;    classification;    topoisomerase;    anticancer;    benzo[c]phenanthridine;    drug design;    pharmacoinformatics;   

DOI  :  10.3390/molecules17044560

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.