In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts - Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun (Frontiers in Chemistry, 2018)
For a drug, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.