Predicting chemical toxicity effects based on chemical-chemical interactions.

ABSTRACT: Toxicity is a major contributor to high attrition rates of new chemical entities in drug discoveries. In this study, an order-classifier was built to predict a series of toxic effects based on data concerning chemical-chemical interactions under the assumption that interactive compounds are more likely to share similar toxicity profiles. According to their interaction confidence scores, the order from the most likely toxicity to the least was obtained for each compound. Ten test groups, each of them containing one training dataset and one test dataset, were constructed from a benchmark dataset consisting of 17,233 compounds. By a Jackknife test on each of these test groups, the 1(st) order prediction accuracies of the training dataset and the test dataset were all approximately 79.50%, sub

SUBMITTER: Chen L 

PROVIDER: S-EPMC3574107 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature