Accelerating Antibiotic Discovery with Large Language Models and Knowledge Graphs
This work addresses the high costs and inefficiencies in antibiotic discovery for pharmaceutical industries, though it is incremental as it builds on existing LLM and KG methods for a specific application.
The researchers tackled the problem of costly antibiotic rediscovery in drug development by proposing an LLM-based pipeline that integrates literature into a Knowledge Graph to detect prior evidence of antibiotic activity, resulting in the identification of 12 negative hits from a list of 73 potential antibiotic-producing organisms to reduce false negatives and accelerate decision-making.
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.