CONF Delmas_ASSOCIATIONFORCOMPUTATIONALLINGUISTICS_2025/IDIAP Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs Delmas, Maxime Wysocka, Magdalena Gusicuma, Danilo Freitas, Andre 63rd Annual Meeting of the Association for Computational Linguistics Vienna 6 693–705 2025 Association for Computational Linguistics https://aclanthology.org/2025.acl-industry.49/ URL https://doi.org/10.18653/v1/2025.acl-industry.49 doi 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 alert system, detecting prior evidence of antibiotic activity to prevent costly rediscoveries. The system integrates literature on organisms and chemicals 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 as well as the user interface for interactive exploration are available at https://github.com/idiap/abroad-kg-store and https://github.com/idiap/abroad-demo-webapp.