Advancing toxicity AI-based prediction with multilevel systems biology: a case study on genotoxicity.

Abstract

The rapid expansion of chemical diversity presents substantial challenges for health and environmental risk assessment, necessitating the development of alternative, high-throughput computational methodologies. A key hurdle in toxicity prediction lies in the heterogeneous nature of adverse health outcomes at the tissue and cellular levels, as biological processes exhibit cell-type-specific and context-dependent responses. Effective prediction of individual-level health effects thus requires the integration of multimodal data, capturing both structural and biological perturbations induced by chemical exposures. We present GenotoxNet, a multimodal deep learning framework that enhances genotoxicity prediction by systematically integrating chemical structures, high-throughput in vitro assay data, and transcriptomics data. By leveraging this multimodal integration, GenotoxNet effectively captures cellular heterogeneity and mechanistic complexity, enabling more comprehensive evaluation of chemical-induced genotoxicity. The model outperformed single-modality approaches, achieving AUCROC of 0.891 ± 0.017 on the internal test set, demonstrating superior predictive capability over models relying solely on chemical structures or individual biological features. The model still performed well on the external chemical set. Beyond classification, GenotoxNet facilitates mechanistic interpretation by aligning multimodal feature representations of genotoxic chemicals with adverse outcome pathway (AOP). This framework not only offers a robust approach for predicting genotoxicity but also aids in the development of preventive strategies and regulatory decisions aimed at mitigating the health risks posed by hazardous chemicals.