HazChemNet: A Deep Learning Model for Hazardous Chemical Prediction

作者：Nan Zhang, Hexiang Qiu, Hongxia Cai, Zhi‐Ru Li, Yutong Li, Zinan Li, Lijuan Qi, Hong-ju Du, Yan Pan, Haiming Jing, Junyu Ning, Bo Xian, Shan Gao · 发表于：International Journal of Molecular Sciences · 年份：2025 · DOI：10.3390/ijms26199288 · 被引用次数：2 · 研究领域：Computational Drug Discovery Methods、Chemistry and Chemical Engineering、Machine Learning in Materials Science

The identification of hazardous chemicals is critical for mitigating environmental and health risks, yet existing methods often lack efficiency and accuracy. This study presents HazChemNet, a deep learning model integrating attention-based autoencoders and mixture-of-experts architectures, designed to predict chemical hazardousness from molecular structures. The study utilized a dataset of 2428 hazardous compounds from China’s 2015 hazardous chemical list. Features were derived from molecular fingerprints and physicochemical descriptors, with external validation on 52 unseen chemicals achieving 92.3% accuracy for hazardous and 84.6% for non-hazardous classifications. Experimental validation using C. elegans assays confirmed model predictions for critical compounds. Ablation studies confirmed hydrogen bonding features as pivotal predictors, alongside molecular fingerprints. This work bridges the gap between AI-driven innovation and chemical safety, offering a transformative tool for sustainable industrial practices and proactive risk management in a rapidly evolving global landscape.