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

Background: 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 unsubstantiated associations and mechanistic inconsistencies. This study aimed to develop BioAgent-RAG, a multi-agent orchestrated retrieval-augmented generation framework tailored for complex biomedical reasoning and transparent graph traversal to resolve this critical trade-off. Methods: The framework integrates autonomous sub-agents for query decomposition, path-based KG grounding, and critic-guided cross-verification to systematically enhance biological plausibility and minimize ungrounded inferences. It was evaluated through rigorous empirical comparisons against competitive baselines (RD2-KG, KGiA, RAG-LLMs, and TxGNN) on the PrimeKG and Hetionet benchmarks, measuring ROC-AUC, PR-AUC, MRR, Recall@10, Recall@50, Biomedical Factuality Score (BFS), and Path Reliability Score (PRS). Results: BioAgent-RAG substantially outperforms existing baselines, achieving on PrimeKG 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, it yields a Biomedical Factuality Score (BFS) of 0.887 on PrimeKG (0.894 on Hetionet) and a Path Reliability Score (PRS) of 0.841 (0.862 on Hetionet), demonstrating strong preliminary correlation (0.912) with domain-expert evaluations. Conclusions: These findings demonstrate that BioAgent-RAG serves as an actionable, clinically explainable computational screening platform capable of de-risking wet-lab pipelines by prioritizing high-confidence repurposing candidates and delivering verifiable multi-hop mechanistic hypotheses directly interpretable by pharmacologists.