Painting Peptides With Antimicrobial Potency Through Deep Reinforcement Learning.

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 diver

SUBMITTER: Dong R 

PROVIDER: S-EPMC12631831 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature