A generative artificial intelligence approach for peptide antibiotic optimization
5 affiliations
- 1Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA USA
- 2Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA USA
- 3Machine 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 USA
- 4Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA USA
- 5Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA USA
Abstract
Antibiotic resistance is rising globally, demanding faster, more reliable routes to design antimicrobial candidates. Although artificial-intelligence-based methods have accelerated antimicrobial discovery, most are designed to screen fixed libraries or generate candidates broadly, rather than optimize existing peptide scaffolds under practical design constraints. Here, to address this challenge, we present APEX generative optimization (ApexGO). ApexGO uses a transformer variational autoencoder that embeds peptide sequences in a continuous latent space, whereas Bayesian optimization efficiently proposes sequence edits to boost antimicrobial potency. Unlike traditional approaches, ApexGO generates peptide sequences through modifications of template peptides, opening avenues for peptide design and antibiotic discovery. Using ten peptides as templates, ApexGO generated optimized derivatives with enhanced antimicrobial properties. We chemically synthesized 100 of these compounds and conducted comprehensive in vitro characterizations, including assessments of antimicrobial activity, mechanism of action, secondary structure and cytotoxicity. In particular, ApexGO achieved an 85% ground-truth experimental hit rate and a 72% success rate in enhancing antimicrobial activity against Gram-negative pathogens, outperforming previously reported methods for antibiotic discovery and optimization. In two preclinical mouse models of Acinetobacter baumannii infection, artificial-intelligence-optimized molecules exhibited potent anti-infective activity superior to their template controls and comparable with or exceeding that of last-resort antibiotic. These findings highlight the potential of ApexGO as a generative artificial intelligence approach for peptide design and antibiotic optimization, offering a powerful tool to accelerate antibiotic discovery.
Related
- How Generative Artificial Intelligence (GAI) reshapes students’ creativity in higher education: A chain path modeling Study from “Exploration–Exploitation” to “Co-Creation–Reflection”
- Perceived GenAI competency and university teachers’ intention to continue using generative artificial intelligence: Extending the technology acceptance model
- Artificial Intelligence in the Fight Against Antimicrobial Resistance: Applications in Diagnosis, Susceptibility Prediction, Precision Dosing, and Antibiotic Discovery.
- On the variability of generative artificial intelligence methods in conceptual modeling: an experimental evaluation on combining causal maps
- Generative artificial intelligence in animal genomics for smart agriculture: Applications, challenges, and future prospects
BibTeX
@article{torres2026generative,
title = {{A generative artificial intelligence approach for peptide antibiotic optimization}},
author = {Marcelo D. T. Torres and Yimeng Zeng and Fangping Wan and Natalie Maus and Jacob R. Gardner and César de la Fuente‐Núñez},
journal = {Nature Machine Intelligence},
year = {2026},
month = may,
doi = {10.1038/s42256-026-01237-5},
url = {https://doi.org/10.1038/s42256-026-01237-5},
note = {CC BY},
}
RIS
TY - JOUR
TI - A generative artificial intelligence approach for peptide antibiotic optimization
AU - Marcelo D. T. Torres
AU - Yimeng Zeng
AU - Fangping Wan
AU - Natalie Maus
AU - Jacob R. Gardner
AU - César de la Fuente‐Núñez
PY - 2026
DA - 2026/05/13
JO - Nature Machine Intelligence
DO - 10.1038/s42256-026-01237-5
UR - https://doi.org/10.1038/s42256-026-01237-5
AB - Antibiotic resistance is rising globally, demanding faster, more reliable routes to design antimicrobial candidates. Although artificial-intelligence-based methods have accelerated antimicrobial discovery, most are designed to screen fixed libraries or generate candidates broadly, rather than optimize existing peptide scaffolds under practical design constraints. Here, to address this challenge, we present APEX generative optimization (ApexGO). ApexGO uses a transformer variational autoencoder that embeds peptide sequences in a continuous latent space, whereas Bayesian optimization efficiently proposes sequence edits to boost antimicrobial potency. Unlike traditional approaches, ApexGO generates peptide sequences through modifications of template peptides, opening avenues for peptide design and antibiotic discovery. Using ten peptides as templates, ApexGO generated optimized derivatives with enhanced antimicrobial properties. We chemically synthesized 100 of these compounds and conducted comprehensive in vitro characterizations, including assessments of antimicrobial activity, mechanism of action, secondary structure and cytotoxicity. In particular, ApexGO achieved an 85% ground-truth experimental hit rate and a 72% success rate in enhancing antimicrobial activity against Gram-negative pathogens, outperforming previously reported methods for antibiotic discovery and optimization. In two preclinical mouse models of Acinetobacter baumannii infection, artificial-intelligence-optimized molecules exhibited potent anti-infective activity superior to their template controls and comparable with or exceeding that of last-resort antibiotic. These findings highlight the potential of ApexGO as a generative artificial intelligence approach for peptide design and antibiotic optimization, offering a powerful tool to accelerate antibiotic discovery.
SN - 2522-5839
ER -