Artificial intelligence-guided phenotypic drug repurposing against Streptococcus pneumoniae
File(s)
Accepted version
Author(s)
Type
Journal Article
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 1,849 actives along with 34,503 inactives, to prospectively examine 6,747 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 nM), 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.
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 1,849 actives along with 34,503 inactives, to prospectively examine 6,747 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 nM), 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.
Date Acceptance
2026-07-14
Citation
Advanced Science
ISSN
2198-3844
Publisher
Wiley
Journal / Book Title
Advanced Science
Copyright Statement
Copyright This paper is embargoed until publication. Once published the Version of Record (VoR) will be available on immediate open access.
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Publication Status
Accepted