首页|East China University of Science and Technology Reports Findings in Machine Learning (In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches)
East China University of Science and Technology Reports Findings in Machine Learning (In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches)
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New research on Machine Learning is the subject of a report. According to news reporting originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicology, there is a growing emphasis on refining, reducing, and replacing animal testing in safety evaluations." Our news editors obtained a quote from the research from the East China University of Science and Technology, "Therefore, the development of robust computational tools is crucial for regulatory applications. The performance of predictive models is heavily reliant on the quality and quantity of data. In this investigation, we amalgamated the most extensive dataset (4901 compounds) sourced from governmental GHS-compliant databases and literature to develop binary classification models of chemical ocular toxicity. We employed 12 molecular representations in conjunction with six machine learning algorithms and two deep learning algorithms to create a series of binary classification models. The findings indicated that the deep learning method GCN outperformed the machine learning models in cross-validation, achieving an impressive AUC of 0.915. However, the top-performing machine learning model (RF-Descriptor) demonstrated excellent performance with an AUC of 0.869 on the test set and was therefore selected as the best model. To enhance model interpretability, we conducted the SHAP method and attention weights analysis. The two approaches offered visual depictions of the relevance of key descriptors and substructures in predicting ocular toxicity of chemicals."
ShanghaiPeople's Republic of ChinaAsiaChemicalsCyborgsEmerging TechnologiesMachine Learning
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