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Olayo-Alarcon, R. ; Amstalden, M.K.* ; Zannoni, A.* ; Bajramovic, M. ; Sharma, C.M.* ; Brochado, A.R.* ; Rezaei, M.* ; Müller, C.L.

Pre-trained molecular representations enable antimicrobial discovery.

Nat. Commun. 16:3420 (2025)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
The rise in antimicrobial resistance poses a worldwide threat, reducing the efficacy of common antibiotics. Determining the antimicrobial activity of new chemical compounds through experimental methods remains time-consuming and costly. While compound-centric deep learning models promise to accelerate this search and prioritization process, current strategies require large amounts of custom training data. Here, we introduce a lightweight computational strategy for antimicrobial discovery that builds on MolE (Molecular representation through redundancy reduced Embedding), a self-supervised deep learning framework that leverages unlabeled chemical structures to learn task-independent molecular representations. By combining MolE representation learning with available, experimentally validated compound-bacteria activity data, we design a general predictive model that enables assessing compounds with respect to their antimicrobial potential. Our model correctly identifies recent growth-inhibitory compounds that are structurally distinct from current antibiotics. Using this approach, we discover de novo, and experimentally confirm, three human-targeted drugs as growth inhibitors of Staphylococcus aureus. This framework offers a viable, cost-effective strategy to accelerate antibiotic discovery.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Drugs
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 16, Heft: 1, Seiten: , Artikelnummer: 3420 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen Bavarian State Ministry of Science and Arts, Germany
This work was funded by a grant for the "StressRegNet" consortium within the Bavarian research network bayresq.net funded through the Bavarian State Ministry of Science and Arts, Germany
DOI 10.1038/s41467-025-58804-4
WOS ID 001464806500038
Scopus ID 105002965389
PubMed ID 40210659
Erfassungsdatum 2025-05-10
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