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In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning

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Abstract

Aquatic toxicity is a crucial endpoint for evaluating chemically adverse effects on ecosystems. Therefore, we developed in silico methods for the prediction of chemical aquatic toxicity in marine environment. At first, a diverse data set including different crustacean species was constructed. We then built local binary models using Mysidae data and global binary models using Mysidae, Palaemonidae, and Penaeidae 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.

Graphical abstract: In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning

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Publication details

The article was received on 06 Dec 2018, accepted on 24 Jan 2019 and first published on 25 Jan 2019


Article type: Paper
DOI: 10.1039/C8TX00331A
Citation: Toxicol. Res., 2019, Advance Article

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    In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning

    L. Liu, H. Yang, Y. Cai, Q. Cao, L. Sun, Z. Wang, W. Li, G. Liu, P. W. Lee and Y. Tang, Toxicol. Res., 2019, Advance Article , DOI: 10.1039/C8TX00331A

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