ArticleFrontiers in toxicology2025
MoltiTox: a multimodal fusion model for molecular toxicity prediction.
Article in Frontiers in toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
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Abstract
Introduction: We introduce MoltiTox, a novel multimodal fusion model for molecular toxicity prediction, designed to overcome the limitations of single-modality approaches in drug discovery. Methods: MoltiTox integrates four complementary data types: molecular graphs, SMILES strings, 2D images, and Results: Evaluated on the Tox21 benchmark across 12 endpoints, MoltiTox achieves a ROC-AUC of 0.831, outperforming all single-modality baselines. Discussion: These findings highlight that integrating diverse molecular representations enhances both the robustness and generalizability of toxicity prediction models. Beyond predictive performance, the inclusion of
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