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

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 s

SUBMITTER: Liu L 

PROVIDER: S-EPMC6505403 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature