Open AccessIn silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning
Author(s) -
Lin Liu,
Hongbin Yang,
Yingchun Cai,
Qianqian Cao,
Lixia Sun,
Zhuang Wang,
Weihua Li,
Guixia Liu,
Philip W. Lee,
Yun Tang
Publication year - 2019
Publication title -
toxicology research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.709
H-Index - 31
eISSN - 2045-4538
pISSN - 2045-452X
DOI - 10.1039/c8tx00331a
Subject(s) - crustacean , in silico , chemical toxicity , aquatic toxicology , toxicity , artificial intelligence , machine learning , acute toxicity , fishery , computer science , biology , chemistry , biochemistry , organic chemistry , gene
Six machine learning methods combined with descriptors or fingerprints were employed to predict chemical toxicity on marine crustaceans.
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