Screening of Respiratory Toxicity of Environmental Compounds Based on Multimodal Feature Fusion Model

The respiratory system constitutes the primary interface between the human body and the external environment, demonstrating particular vulnerability to chemical toxicants through diverse exposure routes. Current regulatory frameworks face significant limitations in respiratory toxicity assessment, relying predominantly on expensive and low-throughput animal testing methods while lacking systematic premarket evaluation protocols. To address these challenges, we developed GFEnet, an innovative multimodal deep learning (DL) framework that synergistically integrates molecular graph features, structural fingerprints, and electron-level properties for comprehensive cross-scale respiratory toxicity prediction. The model was rigorously trained and evaluated across three toxicological dimensions, including in vivo mammalian respiratory toxicity, in vitro A549 cell cytotoxicity, and ACE2 gene regulation activity. GFEnet demonstrated exceptional predictive capability, achieving outstanding AUC values of 0.986, 0.965, and 0.919 on the respective test sets, substantially outperforming conventional machine learning algorithms and single-modality DL architectures. Systematic ablation studies confirmed the critical contribution of each feature modality to the model's predictive power. When applied to screen compounds from substances of very high concern and air pollutant databases, GFEnet identified fluorene-9-bisphenol and Michler's ketone as high-priority risk candidates exhibiting consistent toxicity across all evaluation end points. Subsequent in vivo validation using mouse models confirmed these predictions, demonstrating that both compounds induce significant pulmonary function impairment and histopathological damage. This study establishes GFEnet as a robust high-throughput screening platform for the early identification of respiratory toxicants, effectively bridging computational toxicology with environmental health protection and regulatory science.