<i>De novo</i> generation of peptide binders with desired properties by deep generative models reinforced through enrichment of focused sets for iterative fine-tuning

Recurrent neural networks underwent reinforcement procedures for <i>de novo</i> 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 <i>de novo</i> generation of peptide sequences with high binding affinity to the target and possibly additional properties.