Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs
| Type of publication: | Conference paper |
| Citation: | Delmas_ASSOCIATIONFORCOMPUTATIONALLINGUISTICS_2025 |
| Publication status: | Published |
| 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.... |
| 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. |
| Main Research Program: | AI for Life |
| Additional Research Programs: |
Human-AI Teaming |
| Keywords: | |
| Projects: |
ABRoad |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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