117
0

Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs

Main:5 Pages
9 Figures
Bibliography:2 Pages
3 Tables
Appendix:4 Pages
Abstract

The discovery of novel antibiotics is critical to address the growing antimicrobial resistance (AMR). However, pharmaceutical industries face high costs (over 1billion),longtimelines,andahighfailurerate,worsenedbytherediscoveryofknowncompounds.WeproposeanLLMbasedpipelinethatactsasanalarmsystem,detectingpriorevidenceofantibioticactivitytopreventcostlyrediscoveries.ThesystemintegratesorganismandchemicalliteratureintoaKnowledgeGraph(KG),ensuringtaxonomicresolution,synonymhandling,andmultilevelevidenceclassification.Wetestedthepipelineonaprivatelistof73potentialantibioticproducingorganisms,disclosing12negativehitsforevaluation.Theresultshighlighttheeffectivenessofthepipelineforevidencereviewing,reducingfalsenegatives,andacceleratingdecisionmaking.TheKGfornegativehitsandtheuserinterfaceforinteractiveexplorationwillbemadepubliclyavailable.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.

View on arXiv
@article{delmas2025_2503.16655,
  title={ Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs },
  author={ Maxime Delmas and Magdalena Wysocka and Danilo Gusicuma and André Freitas },
  journal={arXiv preprint arXiv:2503.16655},
  year={ 2025 }
}
Comments on this paper