Endocrine disruptor identification and multitoxicity level assessment of organic chemicals: an example of multiple machine learning models

Endocrine-disrupting chemicals (EDCs) pollution is a major global environmental issue. Assessing the multiple toxic effects of EDCs is key to managing their risks. This study successfully developed an EDCs classification and recognition model based on recursive feature elimination and random forest coupling, which passed external validation. Furthermore, the study classified the hormonal effects of EDCs and elucidated their hormonal roles. Molecular dynamics simulations were employed to investigate the toxicity of EDCs, and a regression model for such toxicity was developed using neural networks. A multi-toxicity regression model for EDCs was also developed using the XGBoost algorithm. This model can evaluate carcinogenicity, teratogenicity, and potential developmental toxicity of EDCs. The Spearman and Kendall correlation coefficient methods were used to assess the relations between toxicities. This study combines data filtering with model optimisation to ensure the use of efficient and concise methods. This allows for a comprehensive assessment of EDCs toxicity. It also helps analyse the link between EDCs molecular structure and their toxic effects, providing ideas for designing new chemicals. However, the model exhibits high complexity, and some processes are difficult to fully explain.