Home
Deutsch
Advanced Search
Browse by ...
Publication overview
Contact persons
Help
 |
|
as soon as is submitted to ZB.
Protein Content-Based Microbial Representations Improve Predictions of Antimicrobial Activity.
In: (34th International Conference on Artificial Neural Networks-ICANN-Annual, 9-12 September 2025, Kaunas, LITHUANIA). Berlin [u.a.]: Springer, 2026. 235-237 (Lect. Notes Comput. Sc. ; 16072)
Deep learning-assisted strategies have enabled the identification of novel antimicrobial compounds with broad-spectrum activity. While these discoveries hold great value, targeted therapies that are effective against specific pathogens and have minimal impact on commensal microbes remain a key objective in the fight against antibiotic resistance. Here, we address this challenge by developing and evaluating proteome-based representations of bacterial species. We find that these representations enable species-resolved predictions of a compound's growth-inhibiting activity. In particular, microbial representations based on the presence of homologous protein groups enable generalizable predictions for unseen compound-microbe pairs. These microbe-specific predictions more accurately recapitulate findings from large-scale chemical screens involving compounds and bacterial pathogens not encountered during model training. We envision that this framework can facilitate the development of targeted therapies.
Additional Metrics?
Edit extra informations
Login
Publication type
Article: Conference contribution
Keywords
Microbe representations; Antimicrobials; Proteome
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
Conference Title
34th International Conference on Artificial Neural Networks-ICANN-Annual
Conference Date
9-12 September 2025
Conference Location
Kaunas, LITHUANIA
Quellenangaben
Volume: 16072,
Pages: 235-237
Publisher
Springer
Publishing Place
Berlin [u.a.]
Institute(s)
Institute of Computational Biology (ICB)