In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning
Lin Liu, Hongbin Yang, Yingchun Cai, Qianqian Cao, Lixia Sun, Zhuang Wang, Weihua Li, Guixia Liu, Philip W. Lee, Yun Tang · Toxicology Research · 2019
data. Molecular fingerprints and descriptors were employed to represent chemical structures separately. All the models were built by six machine learning methods. The AUC (area under the receiver operating characteristic curve) values of the better local and global models were around 0.8 and 0.9 for the test sets, respectively. We also identified several chemicals with selective toxicity on different species. The analysis of selective toxicity would promote to design greener chemicals in a specific environment. Finally, to understand and interpret the models, we explored the relationships between chemical aquatic toxicity and the molecular descriptors. Our study would be helpful in gaining further insights into marine organisms, prediction of chemical aquatic toxicity and prioritization of environmental hazard assessment.