Peer-Reviewed Publication
Sci Transl Med2026;18(871):eaee4629.October 14, 2026Journal Article
From code to cure: Harnessing AI for antibiotic discovery and design.
Aarti Krishnan1,2,3, Hanqun Cao4,5,6,7, James J Collins1,2,3, Cesar de la Fuente-Nunez4,5,6,7
1Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
2Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA.
3Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
4Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
5Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
6Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA.
7Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
Abstract
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 ou…
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