Pre-trained molecular representations enable antimicrobial discovery
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...
Saved in:
| Main Authors: | Roberto Olayo-Alarcon, Martin K. Amstalden, Annamaria Zannoni, Medina Bajramovic, Cynthia M. Sharma, Ana Rita Brochado, Mina Rezaei, Christian L. Müller |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-58804-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Systematic screen uncovers regulator contributions to chemical cues in Escherichia coli
by: Christoph Binsfeld, et al.
Published: (2025-07-01) -
Systematic screen uncovers regulator contributions to chemical cues in Escherichia coli.
by: Christoph Binsfeld, et al.
Published: (2025-07-01) -
Enabling discovery of the social determinants of health: using a specialized lens to see beyond the surface
by: Cynthia Sheffield, et al.
Published: (2025-08-01) -
Lipid discovery enabled by sequence statistics and machine learning
by: Priya M Christensen, et al.
Published: (2024-12-01) -
Advances in high-pressure materials discovery enabled by machine learning
by: Zhenyu Wang, et al.
Published: (2025-05-01)