BioScientist Agent: Designing LLM-Biomedical Agents with KG-Augmented RL Reasoning Modules for Drug Repurposing and Mechanistic of Action Elucidation

Drug discovery is protracted, resource-intensive, and afflicted by attrition rates exceeding 90%, which leaves most diseases, particularly rare or neglected indications, without effective therapies. Drug repurposing offers a cost effective alternative, yet systematic identification of novel drug indication pairs and mechanistic rationales remains hindered by the scale and heterogeneity of biomedical knowledge. We present BioScientist Agent, an end to end framework that unifies a billion-fact biomedical knowledge graph with (i) a variational graph auto-encoder for representation learning and link prediction driven repositioning, and (ii) a reinforcement learning module that traverses the graph to recover biologically plausible mechanistic paths. (iii) A large language model (LLM) multi-agent layer orchestrates these components, enabling inference of target pathways for a drug disease pair, and automatic generation of coherent causal reports. Across benchmark datasets BioScientist Agent surpasses state of the art baselines in both accuracy and recall, while furnishing mechanistic explanations that align with curated literature. Its open and modular design accelerates hypothesis generation and reduces experimental overhead in early-stage discovery.