Integrating Diffusion and Liquid AI Models for Predicting Peptide Affinity from mRNA Display Selections

In vitro selection and directed evolution technologies such as mRNA display, explore large libraries ([≥]1014 variants) and generate thousands to millions of functional polypeptide ligands to a variety of targets. Denoising diffusion implicit machine learning models (DDIMs) trained using display-derived deep sequencing data can greatly expand these functional sequences beyond what is accessible experimentally. However, methods are needed to predict peptide properties such as binding free energies ({Delta}G{degrees}). Here, we applied machine learning methods to predict binding free energies of both experimental and DDIM-generated peptide ligands against a target of interest, the oncogenic