Ai derivation and exploration of antibiotic class spaces
Our fragment-based pipeline demonstrates the feasibility of systematically exploring antibiotic chemical spaces through computational reconstruction of historical development pathways and generation of hybrid molecules with with predicted multi-target binding profiles. All results represent computational predictions requiring experimental validation.
Authors
- Adam Bess (ORCID: https://orcid.org/0000-0003-1156-076X)
- Chris Alvin (ORCID: https://orcid.org/0000-0001-6044-2159)
- Sean Rowland
- Supratik Mukhopadhyay
Institutions
- Louisiana State University (US)
- Furman University (US)
Publication Details
- Journal
- Journal of Cheminformatics
- Published
- 2026-01-16
- DOI
- https://doi.org/10.1186/s13321-026-01153-1
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00