%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Delmas_ASSOCIATIONFORCOMPUTATIONALLINGUISTICS_2025,
author = {Delmas, Maxime and Wysocka, Magdalena and Gusicuma, Danilo and Freitas, Andre},
projects = {ABRoad},
mainresearchprogram = {AI for Life},
additionalresearchprograms = {Human-AI Teaming},
title = {Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs},
journal = {In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics},
booktitle = {63rd Annual Meeting of the Association for Computational Linguistics},
volume = {6},
year = {2025},
pages = {693–705},
publisher = {Association for Computational Linguistics},
location = {Vienna},
url = {https://aclanthology.org/2025.acl-industry.49/},
doi = {https://doi.org/10.18653/v1/2025.acl-industry.49},
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 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.}
}