Multimodal Cross‐Attentive Graph‐Based Framework for Predicting In Vivo Endocrine Disruptors

ABSTRACT Endocrine hazard assessment needs models that are accurate and mechanistically transparent. We present a multimodal cross‐attentive graph framework that fuses molecular graphs with adverse‐outcome‐pathway (AOP)–anchored assay signals to predict organism‐level outcomes in the organisation for economic co‐operation and development (OECD) Hershberger and uterotrophic assays. In Tier‐1, multitask graph neural networks (GNNs) learn estrogen and androgen receptor molecular‐initiating and key events across 46 in vitro ToxCast/Tox21 assays. In Tier‐2, a cross‐attentive multimodal GNN integrates Tier‐1 pathway signals with molecular graphs, yielding high predictive performance for both the in vivo Hershberger (AUROC = 0.97 ± 0.014) and uterotrophic (AUROC = 0.97 ± 0.008) assays. Retrospective analysis of literature compounds showed 88% concordance (Hershberger 15/18; uterotrophic 23/26). Bidirectional cross‐attention highlights associations between molecular substructures and pathway‐level assay nodes, while counterfactual perturbations rank assays and structural motifs most influential for each decision. The framework couple's high accuracy with assay‐traceable explanations, supporting targeted testing within the integrated approaches.