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De novo generation of peptide binders with desired properties by deep generative models reinforced through enrichment of focused sets for iterative fine-tuning
- Oh, Doogie;
- Kim, Jongseong;
- Park, Yongdoo
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0초록
Recurrent neural networks underwent reinforcement procedures for de novo generation of peptide binders with desired properties. Docking and scoring of peptides from these models allowed enrichment of focused sets with validated sequences for iterative fine-tuning, leading to reinforcement of those models. They enabled de novo generation of peptide sequences with high binding affinity to the target and possibly additional properties.
키워드
Peptides; Binding Affinities; Fine Tuning; Generative Model; Neural-networks; Peptide Sequences; Property; Recurrent Neural Networks; Amino Acid Sequence; Animal Experiment; Animal Model; Article; Binding Affinity; Controlled Study; Drug Analysis; Generative Model; Mouse; Nonhuman; Recurrent Neural Network; Artificial Neural Network; Chemistry; Metabolism; Molecular Docking; Peptide; Protein Binding; Molecular Docking Simulation; Neural Networks, Computer; Peptides; Protein Binding; DESIGN; LIGAND
- 제목
- De novo generation of peptide binders with desired properties by deep generative models reinforced through enrichment of focused sets for iterative fine-tuning
- 저자
- Oh, Doogie; Kim, Jongseong; Park, Yongdoo
- 발행일
- 2025-05-23
- 유형
- Article; Early Access
- 권
- 61
- 호
- 52
- 페이지
- 9488 ~ 9491