From code to cure: Harnessing AI for antibiotic discovery and design
Artificial intelligence (AI) now enables the exploration of vast chemical and sequence spaces, the design of synthesizable antibiotics, and the prioritization of safe, mechanism-diverse candidates through integrated modeling and accelerated testing. Here, we survey recent advances in AI for both small-molecule and peptide antimicrobial drugs, highlight emerging data and reporting standards, and outline translational pathways and regulatory hurdles needed to bring these agents to patients.
Authors
- Aarti Krishnan (ORCID: https://orcid.org/0000-0002-7936-4442)
- César de la Fuente‐Núñez (ORCID: https://orcid.org/0000-0002-2005-5629)
- Hanqun Cao (ORCID: https://orcid.org/0000-0002-8104-1845)
- James J. Collins (ORCID: https://orcid.org/0000-0002-5560-8246)
Institutions
- Broad Institute (US)
- Harvard University (US)
- Wyss Institute for Biologically Inspired Engineering
- Massachusetts Institute of Technology (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Science Translational Medicine
- Published
- 2026-10-14
- DOI
- https://doi.org/10.1126/scitranslmed.aee4629
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Science and ICT, South Korea
- Defense Threat Reduction Agency