A scalable reinforcement learning approach for screening large peptide libraries for bioactive peptide discovery.

ABSTRACT: Bioactive peptides such as anticancer peptides (ACPs) offer a promising therapeutic alternative to small molecules due to their efficiency and selectivity against tumors and minimal toxicity towards healthy human cells. However, their rational discovery requires navigating a vast chemical space using computationally demanding in silico tools. Herein, we present a computational method enabling cost-efficient exploration of large peptide libraries using reinforcement learning and posterior sampling. Practical application of the developed approach results in identification of membranolytic peptides with therapeutic potential. The developed computational method reduces the search space by over 90% compared to exhaustive library screening and enables effective balancing between dataset's explor

SUBMITTER: Pandey M 

PROVIDER: S-EPMC12748989 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature