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Critical Features Identification for Chemical Chronic Toxicity Based on Mechanistic Forecast Models by Xiaoqing Wang; Fei Li; Jingwen Chen; Yuefa Teng; Chenglong Ji; Huifeng Wu is a Environmental Science article available to read on EtoBox.

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Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cause potential adverse effects. However, chronic aquatic toxicity effects of chemicals are much scarcer, especially at population level. Rotifers are highly sensitive to toxicants even at chronic lowdoses and their communities are usually considered as effective indicators for assessing the status of aquatic ecosystems. Therefore, the no observed effect concentration (NOEC) for population abundance of rotifers were selected as endpoints to develop machine learning models for the prediction of chemical aquatic chronic toxicity. In this study, forty-eight binary models were built by eight types of chemical descriptors combined with six machine learning algorithms. The best binary model was 1D & 2D molecular descriptorsrandom trees model (RT) with high balanced accuracy (BA) (0.83 for training and 0.83 for validation set), and Matthews correlation coefficient (MCC) (0.72 for training set and 0.67 for validation set). Moreover, the optimal mo

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Author

Xiaoqing Wang; Fei Li; Jingwen Chen; Yuefa Teng; Chenglong Ji; Huifeng Wu

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Environmental Science (Physical Sciences)