Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs

• URL: http://arxiv.org/abs/2503.16655v2
• Date: Thu, 27 Mar 2025 16:26:55 GMT
• Title: Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs
• Authors: Maxime Delmas, Magdalena Wysocka, Danilo Gusicuma, André Freitas,
• Abstract summary: We propose an LLM-based pipeline that acts as an alarm system, detecting prior evidence of antibiotic activity to prevent costly rediscoveries.<n>The system integrates organism and chemical literature into a Knowledge Graph (KG), ensuring taxonomic resolution, synonym handling, and multi-level evidence classification.<n>The results highlight the effectiveness of the pipeline for evidence reviewing, reducing false negatives, and accelerating decision-making.
• Score: 8.808991968847693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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