A support vector machine classification model for benzo[c]phenathridine analogues with toposiomerase-I inhibitory activity

Molecules. 2012 Apr 17;17(4):4560-82. doi: 10.3390/molecules17044560.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Models, Theoretical
  • Phenanthrenes / chemistry*
  • Phenanthrenes / pharmacology*
  • Reproducibility of Results
  • Support Vector Machine*
  • Topoisomerase I Inhibitors / chemistry*
  • Topoisomerase I Inhibitors / pharmacology

Substances

  • Phenanthrenes
  • Topoisomerase I Inhibitors