drug-drug-interaction

This paper proposes CMF-ELN, a cross-modal fusion end-to-end learning network for cold-start drug-drug interaction prediction, using multi-modal knowledge graphs and a four-channel graph autoencoder to improve similarity modeling and interpretability.

Proposes DDIAgents, a mechanism-conditioned multi-agent framework for drug-drug interaction prediction that dynamically routes relevant biomedical knowledge to specialized expert agents and aggregates their analyses, outperforming existing feature-based, graph-based, and LLM-based methods.

Introduces MARD, a 7B-parameter model for mechanism-level drug-drug interaction prediction using mirror-augmented reasoning distillation, achieving state-of-the-art accuracy at ~1% of frontier API cost and demonstrating genuine pharmacological reasoning over memorization.