AquaticTox: A Web-Based Tool for Aquatic Toxicity Evaluation Based on Ensemble Learning to Facilitate the Screening of Green Chemicals
The widespread use of chemical products inevitably brings many side effects as environmental pollutants. Toxicological assessment of compounds to aquatic life plays an important role in protecting the environment from their hazards. However, in vivo animal testing approaches for aquatic toxicity evaluation are time-consuming, expensive, and ethically limited, especially when there are a great number of compounds. In silico modeling methods can effectively improve the toxicity evaluation efficiency and save costs. Here, we present a web-based server, AquaticTox, which incorporates a series of ensemble models to predict acute toxicity of organic compounds in aquatic organisms, covering Oncorhynchus mykiss, Pimephales promelas, Daphnia magna, Pseudokirchneriella subcapitata, and Tetrahymena pyriformis. The predictive models are built through ensemble learning algorithms based on six base learners. These ensemble models outperform all corresponding single models, achieving area under the curve (AUC) scores of 0.75-0.92. Compared to the best single models, the average precisions of the ensemble models have been increased by 12-22%. Additionally, a self-built knowledge base of the structure-aquatic toxic mode of action (MOA) relationship was integrated into AquaticTox for toxicity mechanism analysis. Hopefully, the user-friendly tool (https://chemyang.ccnu.edu.cn/ccb/server/AquaticTox); could facilitate the identification of aquatic toxic chemicals and the design of green molecules.
