Predicting Chemical Toxicity Effects Based on Chemical-Chemical Interactions

Chen, Lei; Lu, Jing; Zhang, Jian; Feng, Kai-Rui; Zheng, Ming-Yue; Cai, Yu-Dong
February 2013
PLoS ONE;Feb2013, Vol. 8 Issue 2, p1
Academic Journal
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 1st order prediction accuracies of the training dataset and the test dataset were all approximately 79.50%, substantially higher than the rate of 25.43% achieved by random guesses. Encouraged by the promising results, we expect that our method will become a useful tool in screening out drugs with high toxicity.


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