Explainable Reasoning Path Inference of Anti-Cancer Drug Sensitivity on Genomic Knowledge Graph via Macro-Micro Agent Collaborative Reinforcement Learning

Artificial intelligence (AI) based anticancer drug recommendation systems have emerged as powerful tools for precision dosing. Although existing methods have advanced in terms of predictive accuracy, they encounter three significant obstacles, including the "black-box" problem resulting in unexplainable reasoning, the computational difficulty for graph-based structures, and the combinatorial explosion during multi-step reasoning. To tackle these issues, we introduce a novel Macro-Micro agent Drug sensitivity inference (MarMirDrug). Specifically, our methodology enhances interpretability via knowledge graphs (KG). To reduce computational overhead, our MarMirDrug also transforms the graph structures of KG into low-dimensional embeddings. To manage the combinatorial explosion of graph paths that occurs with increasing inference steps, we incorporate a macro-micro dual-agent reinforcement learning algorithm, and combines reinforcement learning with KG-based reasoning to infer path reasoning. In computational experimental outcomes, the efficiency of our embedding model surpasses that of the baseline model, achieving an average improvement of 117.65% for hit score. Also, our dual-agent framework exhibits superior performance and interpretability in drug response prediction, with AUC values outperforming conventional baseline methods. In summary, our approach integrates interpretability, computational efficiency, and predictive accuracy, providing novel contributions to precision medicine.