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.

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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
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article

Ai derivation and exploration of antibiotic class spaces

Adam Bess, Chris Alvin, Sean Rowland, Supratik Mukhopadhyay
Journal of Cheminformatics
Computational Drug Discovery Methods
article

Ai derivation and exploration of antibiotic class spaces

Adam Bess, Chris Alvin, Sean Rowland, Supratik Mukhopadhyay
article en

Abstract

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.

Journal of CheminformaticsVol. 18(1)
Louisiana State University (US), Furman University (US)
Openalex Percentile: Top 84%
Computational Drug Discovery Methods
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