Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides.

ABSTRACT: Using amino acid residues in peptide generation has solved several key problems, including precise control of amino acid sequence order, customized peptides for property modification, and large-scale peptide synthesis. Proteins contain unknown amino acid residues. Extracting them for the synthesis of drug-like peptides can create novel structures with unique properties, driving drug development. Computer-aided design of novel peptide drug molecules can solve the high-cost and low-efficiency problems in the traditional drug discovery process. Previous studies faced limitations in enhancing the bioactivity and drug-likeness of polypeptide drugs due to less emphasis on the connection relationships in amino acid structures. Thus, we proposed a reinforcement learning-driven generation model bas

SUBMITTER: Wang Q 

PROVIDER: S-EPMC11387070 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature