用大模型和知识图谱加速抗生素发现，避免重复研发。
Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs
- 构建知识图谱融合微生物与化学文献，自动识别已知活性化合物
- 在73个候选菌株中发现12个无抗生素活性的负面案例
- 可公开共享知识图谱与交互界面，助力科研人员高效筛选
抗生素新药发现对应对日益严重的抗菌药物耐药性（AMR）至关重要。然而制药行业面临成本超10亿美元、周期长且失败率高，还常重复发现已知化合物。我们提出一种基于大语言模型的流水线，作为预警系统，检测已有抗生素活性证据，防止昂贵的重复研究。该系统将生物体与化学文献整合为知识图谱（KG），实现分类学精准定位、同义词处理及多层级证据分类。我们在一个包含73个潜在产抗生素微生物的私有列表上测试了该流程，揭示了12个负面结果用于评估。结果表明，该流程在证据审查中有效减少假阴性，加速决策。负面案例的知识图谱及交互式用户界面将公开发布。
原文摘要 · Abstract (English)
The discovery of novel antibiotics is critical to address the growing antimicrobial resistance (AMR). However, pharmaceutical industries face high costs (over $1 billion), long timelines, and a high failure rate, worsened by the rediscovery of known compounds. We propose an LLM-based pipeline that acts as an alarm system, detecting prior evidence of antibiotic activity to prevent costly rediscoveries. The system integrates organism and chemical literature into a Knowledge Graph (KG), ensuring taxonomic resolution, synonym handling, and multi-level evidence classification. We tested the pipeline on a private list of 73 potential antibiotic-producing organisms, disclosing 12 negative hits for evaluation. The results highlight the effectiveness of the pipeline for evidence reviewing, reducing false negatives, and accelerating decision-making. The KG for negative hits and the user interface for interactive exploration will be made publicly available.
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