BioAgent-RAG: A Knowledge-Graph-Augmented Multi-Agent LLM Framework with Self-Reflective Verification for Explainable Drug Repurposing

Biomedical Knowledge Graphs (KGs) such as PrimeKG and Hetionet offer vast structured repositories for clinical discovery; however, conventional Graph Neural Networks (GNNs) suffer from black-box opacity, while standard Retrieval-Augmented Generation (RAG) paradigms face catastrophic hallucinations and biological implausibility. To resolve this critical trade-off, this paper proposes BioAgent-RAG, a multi-agent orchestrated retrieval-augmented generation framework tailored for complex biomedical reasoning and transparent graph traversal. By integrating autonomous sub-agents for query decomposition, path-based KG grounding, and cross-verification, BioAgent-RAG systematically enhances biological plausibility and reduces reasoning hallucinations. Comprehensive empirical evaluations demonstrate that BioAgent-RAG substantially outperforms existing competitive baselines, RD2-KG, KGiA, and RAG-LLMs. Specifically, on the PrimeKG benchmark, it achieves an ROC-AUC of 0.952, PR-AUC of 0.814, MRR of 0.637, and superior Recall@10 and Recall@50 of 0.715 and 0.868, respectively (p < 0.01). Moreover, evaluated on biological plausibility, BioAgent-RAG yields a Biomedical Factuality Score (BFS) of 0.887 on PrimeKG (0.894 on Hetionet) and a Path Reasoning Score (PRS) of 0.841 (0.862 on Hetionet), demonstrating strong preliminary correlation (0.912) with domain-expert evaluations. These findings suggest that BioAgent-RAG serves as a robust, explainable computational screening tool to prioritize drug repurposing candidates prior to in vitro validation.