Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds

He, Ying; Liu, Guohong; Hu, Song; Wang, Xiaohong; Jia, Jianbo; Zhou, Hongyu; Yan, Xiliang

doi:10.1016/j.jhazmat.2023.131942

Cited by 5 publications

(1 citation statement)

References 51 publications

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“…DL has been increasingly applied in various research domains. While in many instances, DL models outperform traditional ML models in compound property prediction, , it is important to note that when the data set is limited in size, ML often yields better results than DL. ,, Furthermore, DL algorithms are considered to perform well on large data sets, as they can learn more complex features from the training set . In the models we constructed, due to the use of undersampling methods, the modeling data rely on a limited number of samples.…”

Section: Discussionmentioning

confidence: 99%

In Silico Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods

Lou,

Yu,

Huang

et al. 2024

Chem. Res. Toxicol.

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The research on acute dermal toxicity has consistently been a crucial component in assessing the potential risks of human exposure to active ingredients in pesticides and related plant protection products. However, it is difficult to directly identify the acute dermal toxicity of potential compounds through animal experiments alone. In our study, we separately integrated 1735 experimental data based on rabbits and 1679 experimental data based on rats to construct acute dermal toxicity prediction models using machine learning and deep learning algorithms. The best models for the two animal species achieved AUC values of 78.0 and 82.0%, respectively, on 10-fold cross-validation. Additionally, we employed SARpy to extract structural alerts, and in conjunction with Shapley additive explanation and attentive FP heatmap, we identified important features and structural fragments associated with acute dermal toxicity. This approach offers valuable insights for the detection of positive compounds. Moreover, a standalone software tool was developed to make acute dermal toxicity prediction easier. In summary, our research would provide an effective tool for acute dermal toxicity evaluation of pesticides, cosmetics, and drug safety assessment.

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