Screening toxic transformation products of emerging pollutants in advanced oxidation processes with 3D deep learning...

The rapid and precise toxicity assessment of chemical pollutants and their byproducts formed during water treatment and in aquatic environments remains a significant environmental challenge, as the predictive power of conventional quantitative structure-activity relationship (QSAR) models is limited by their reliance on simplified molecular descriptors. To address this, ToxD4C, a novel multi-modal deep learning framework, was developed to simultaneously classify and regress 31 toxicity endpoints, covering nuclear receptor and enzyme panels, stress response assays, mutagenicity, carcinogenicity, cardiopulmonary toxicity, and various environmental toxicities of concern for water quality management. ToxD4C uniquely integrates three-dimensional molecular geometries, graph attention networks, and SE(3)-equivariant Transformer architectures, effectively capturing complex stereochemical and electronic molecular features. In parallel, a pretrained Uni-Mol model was fine-tuned via transfer learning on Density Functional Theory (DFT)-optimized structures, independently generating normalized toxicity predictions with enhanced reliability and generalization. Both approaches outperformed traditional descriptor-based models across validation tests. Feature‑attribution analysis (SHAP) highlighted key physicochemical drivers of predicted toxicity, and receptor docking offered mechanistic context for selected receptor‑mediated endpoints. Applied to realistic UV/H₂O₂ advanced oxidation scenarios in a real water matrix, this approach efficiently identified high-risk transformation products, and their predicted toxicity was further validated in vitro using JC-1 mitochondrial membrane potential, CCK-8 cell viability, and nuclear receptor/stress-response reporter assays. These tools are integrated within the open-source Tox-Agents platform, enabling rapid and interpretable decision-making for water treatment and environmental risk assessment.