Design of permeability-optimized target-binding macrocycles via direct preference optimization

Abstract

Macrocyclic peptides represent a promising therapeutic modality for challenging targets, such as protein-protein interactions. However, their clinical utility is often limited by inadequate membrane permeability, which restricts both intracellular target access and oral bioavailability. Existing structure-based generative methods for cyclic peptide design prioritize structural validity and binding affinity, yet lack mechanisms to co-optimize membrane permeability. Here we present CycDiff-DPO, a preference-aligned diffusion framework for designing target-specific macrocyclic peptide binders with optimized membrane permeability. By ranking sampled candidates with a Caco-2 permeability predictor and constructing preference pairs, CycDiff-DPO aligns the generative distribution toward permeability-favorable chemical space while preserving target binding competence. We benchmarked CycDiff-DPO across 56 protein targets, finding higher predicted Caco-2 and PAMPA permeability across multiple independent predictors, alongside superior binding energetics and comparable stereochemical quality relative to baseline methods. Case studies on Keap1-Nrf2 and SPSB2-iNOS confirm that top designs recapitulate hot-spot interactions and maintain stable bound poses in molecular dynamics simulations. CycDiff-DPO provides a framework for permeability-enhanced macrocyclic peptide design with broad therapeutic applications.