HazChemNet: A Deep Learning Model for Hazardous Chemical Prediction.

ABSTRACT: 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

SUBMITTER: Zhang N 

PROVIDER: S-EPMC12524297 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature