Artificial Intelligence-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae
Abstract
The prevalence of antimicrobial resistance (AMR) within the common bacterial pathogen Streptococcus pneumoniae makes it a priority for the development of new antibiotics. While artificial intelligence (AI) has recently boosted phenotype-based repurposing of drugs for other human pathogens, this remains to be investigated for S. pneumoniae. Thus, we leveraged ensembles of transformer, graph, and tree models, each trained on a set of 1849 actives along with 34 503 inactives, to prospectively examine 6747 drugs. Of 11 selected candidate antibiotics, nine were found to strongly reduce in vitro growth of S. pneumoniae R6, with IC50 values of ≤ 0.4 µg/mL. The most potent drugs, thiostrepton and ceftiofur, had IC50 values of 0.0001 µg/mL (60.1 pM) and 0.0004 µg/mL (764 pM), respectively. Thiostrepton remained highly potent even against multidrug-resistant strains, suggesting it could be effectively deployed to treat common non-invasive S. pneumoniae infection as part of antibiotic stewardship efforts.
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