Pre-trained molecular representations enable antimicrobial discovery

Roberto Olayo-Alarcon (), Martin K. Amstalden, Annamaria Zannoni, Medina Bajramovic, Cynthia M. Sharma, Ana Rita Brochado, Mina Rezaei and Christian L. Müller ()
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Roberto Olayo-Alarcon: Ludwig-Maximilians-Universität München
Martin K. Amstalden: Julius-Maximilians-Universität Würzburg
Annamaria Zannoni: Julius-Maximilians-Universität Würzburg
Medina Bajramovic: Ludwig-Maximilians-Universität München
Cynthia M. Sharma: Julius-Maximilians-Universität Würzburg
Ana Rita Brochado: Julius-Maximilians-Universität Würzburg
Mina Rezaei: Ludwig-Maximilians-Universität München
Christian L. Müller: Ludwig-Maximilians-Universität München

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-58804-4

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