SL-MERK: Synthetic Lethality Mechanism Explainer based on GraphRAG and Knowledge Graph
Xueheng Lv, Yimiao Feng, Jie Zheng · 2025
Synthetic lethality (SL) holds great promise as an emerging strategy of cancer therapy by selectively eliminating cancer cells. Despite wide adoption of high-throughput technologies for SL screening, the limited understanding of SL mechanisms poses significant challenges to its clinical application. Consequently, computational methods for uncovering SL mechanisms are of considerable value. In recent years, the widespread adoption and rapid advancement of large language models (LLMs) have made artificial intelligence-driven explanations of SL mechanisms increasingly feasible and reliable. In this paper, we propose a novel SL mechanism explanation framework based on LLMs that integrates and complements GraphRAG (Graph Retrieval-Augmented Generation) with knowledge graphs. Our approach named SL-MERK combines the SL interaction patterns extracted by GraphRAG from the biomedical literature with the rich mechanistic knowledge encoded in knowledge graphs. Leveraging the capabilities of LLMs for generalization and natural language generation, this framework generates comprehensive and interpretable natural language explanations of SL mechanisms. Furthermore, experimental evaluations demonstrate that our framework significantly outperforms GPT-4 and several other LLMs in explanatory performance.