Full-Atom Peptide Design with Dual-Flow Contrastive Guidance

Peptides can bind protein surfaces with high specificity and engage interfaces that are typically hard to drug, making them a highly promising class of therapeutics. Recent advances in structure-based peptide design leverage diffusion and flow-matching models to simultaneously generate binding peptide sequences and structures. While existing methods can optimize binding affinities through property guidance or preference alignment, they require extensive model retraining or auxiliary classifiers, and still struggle with poor side-chain modeling and severe steric clashes. To address these limitations, we introduce a lightweight and transferable approach that concurrently optimizes binding energy and side-chain conformations. Specifically, we propose PepContra, a dual-flow contrastive guidance method for full-atom peptide co-design. This contrastive guidance integrates dual flows during sampling, leveraging their difference to drive the generative process toward high-quality regions and away from suboptimal spaces. We further optimize the joint sampler by synchronizing discrete amino-acid types with continuous side-chain torsions at each Euler step. Experimental results demonstrate that PepContra outperforms existing approaches across co-design benchmarks, achieving superior binding affinities and highly competitive stereochemistry.