Single-task regression naturally adapts to multi-species (eco)toxicological modelling: a case study on animals

In silico (eco)toxicological modelling has gained increasing popularity with chemical environmentalists in accelerating toxicity assessment of hazardous chemicals on environments, animal well-being and human health. Existing local and multi-task models commonly exhibit restricted extensibility in multi-species modelling scenarios. In this work, we propose a strategy of single-task regression to naturally adapt modelling to (eco)toxicological measurements on multiple species without requiring a certain number of common pesticides among tested species as multi-task regression does. This strategy treats all species equally in an integral model to facilitate data augmentation and inter-species transfer of common patterns of fragmental toxicities. We aggregate 37,305 measurements of 29,140 pesticides on 10 tested groups of animals to train four machine learning models including extreme gradient boosting (XGBoost), deep neural networks (DNN), random forest (RF) and support vector regression (SVR). Five-fold stratified cross-validation shows that the XGBoost outperforms the other three models with overall 0.67 R², 0.44 RMSE and 0.29 MAE. As compared to local models focusing on one animal group, the proposed single-task regression model achieves a 0.08 ~ 0.49 R² increase. XGBoost feature importance shows that Morgan bit 389 (five-atom fraction of the aromatic ring) exhibits top importance to single-task regression and single-animal regression. Lastly, taking the pesticide parathion and dimethoate as control baselines, we demonstrate the credibility of several case studies from the viewpoints of toxicity profile similarities and pesticide structural similarities.