Cited by 11Antimicrobial Peptides and ActivitiesComputational Drug Discovery MethodsChemical Synthesis and Analysis

Painting Peptides With Antimicrobial Potency Through Deep Reinforcement Learning

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Dong, Ruihan, Cao, Qiushi, Song, Chen·2025·Advanced Science

Published version of a preprint

Abstract

In the post‐antibiotic era, antimicrobial peptides (AMPs) are considered ideal drug candidates because of their lower likelihood of inducing resistance. Computational models provide an efficient way to design novel AMPs. However, current optimization and generation approaches are tailored for specific application scenarios, which hinders the ease of use. To address this challenge, a novel AMP design model named AMPainter is proposed. Based on deep reinforcement learning, AMPainter integrates optimization and generation tasks in a unified framework. AMPainter is applied to three types of peptides, including known AMPs, signal peptides (SPs), and random sequences. AMPainter outperforms ten related models in enhancing the activity of known AMPs on the predicted antimicrobial potency and diversity. Several AMPs demonstrate a 128‐fold decrease in their actual minimal inhibitory concentrations (MICs). AMPainter evolves effective AMPs from membrane‐active SPs with an experimental success rate of 80%. In terms of generation, de novo designed AMP from an inactive random sequence achieves an average MIC of 2.88 µM against four bacteria. In vitro MICs of peptides along the virtual evolutionary path match the predicted scores. Therefore, AMPainter can significantly improve the antimicrobial potency of various peptides, expand the AMP sequence space, and discover novel antimicrobial agents.

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Authors
Dong, R., Cao, Q., & Song, C.
Affiliations
Peking University (CN)
Corresponding author
Song, Chen
Venue
Advanced Science
Publisher
Wiley
Published
2025-09-12
Volume
12
Issue
43
Pages
e06332-e06332
ISSN
2198-3844 (ISSN-L)
OpenAlex
W4414162912
Cited by
11
References
67
Citations by year
2026: 9 · 2025: 2
FWCI
5.98
Type
journal article
Language
en
Open access
Locations
  • Advanced Science (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Key Research and Development Program of China (2024YFA0916800)
  • Innovative Research Group Project of the National Natural Science Foundation of China (T2321001)
Topics
  • Life Sciences › Immunology and Microbiology › Microbiology › Antimicrobial Peptides and Activities
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
Keywords
antimicrobial peptides, peptide design, deep reinforcement learning, signal peptide, minimum inhibitory concentration, antimicrobial potency, de novo peptide design, antibiotic resistance, post-antibiotic era
MeSH terms
Deep Learning, Antimicrobial Peptides, Reinforcement Machine Learning, Anti-Infective Agents, Anti-Bacterial Agents, Bacteria, Microbial Sensitivity Tests, Drug Design

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