Evidence map›Paper›PMID 42745629›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Artificial Intelligence-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae.

Joshua S Fitch, Muhammad D Ariadi, Min Jung Kwun, Leonie Howells, Saiveth Hernandez-Hernandez, Nicholas J Croucher, Pedro J Ballester

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Joshua S FitchImperial College London, Department of Bioengineering, Sir Michael Uren Hub, White City campus, London, UK.ORCID https://orcid.org/0009-0002-5924-2306
Muhammad D AriadiImperial College London, Department of Bioengineering, Sir Michael Uren Hub, White City campus, London, UK.
Min Jung KwunMRC Centre for Global Infectious Disease Analysis, School of Public Health, Sir Michael Uren Hub, White City campus, London, UK.ORCID https://orcid.org/0000-0003-1771-6913
Leonie HowellsMRC Centre for Global Infectious Disease Analysis, School of Public Health, Sir Michael Uren Hub, White City campus, London, UK.
Saiveth Hernandez-HernandezInserm UMR 1307, CRCI2NA, Nantes Université, Nantes, France.
Nicholas J CroucherMRC Centre for Global Infectious Disease Analysis, School of Public Health, Sir Michael Uren Hub, White City campus, London, UK.ORCID https://orcid.org/0000-0001-6303-8768
Pedro J BallesterImperial College London, Department of Bioengineering, Sir Michael Uren Hub, White City campus, London, UK.ORCID https://orcid.org/0000-0002-4078-743X

Funding

The Royal Society and the Wolfson Foundation RSWF∖R1∖221005UK Medical Research Council and Department for International Development MR/R015600/1UK Medical Research Council and Department for International Development MR/T016434/1UK Research and Innovation EP/Y030974/1Wellcome and the Royal SocietyWellcome Trust
6 · The paper itself

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 IC

Indexed as

antimicrobial resistanceartificial intelligencedrug resistantstreptococcus pneumoniae

Identifiers

PMID42745629
PMCPMC13579117

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.