Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning.

ABSTRACT: While the useful armory of antibiotic drugs is continually depleted due to the emergence of drug-resistant pathogens, the development of novel therapeutics has also slowed down. In the era of advanced computational methods, approaches like machine learning (ML) could be one potential solution to help reduce the high costs and complexity of antibiotic drug discovery and attract collaboration across organizations. In our work, we developed a large antimicrobial knowledge graph (AntiMicrobial-KG) as a repository for collecting and visualizing public in vitro antibacterial assay. Utilizing this data, we build ML models to efficiently scan compound libraries to identify compounds with the potential to exhibit antimicrobial activity. Our strategy involved training seven classic ML models

SUBMITTER: Gadiya Y 

PROVIDER: S-EPMC11898080 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature