PepGuide: Interaction-Guided Latent Diffusion for Target-Conditioned Peptide Design

Target-conditioned peptide design poses a significant challenge in drug discovery, primarily because achieving productive peptide–target binding is subject to stringent constraints: the peptide must not only be geometrically plausible, but also establish complementary interactions with the target at the binding interface. However, existing de novo design methods struggle to satisfy these binding-critical constraints simultaneously, resulting in low binding success rates. In this paper, we propose a novel Interaction-Guided Latent Diffusion Framework (PepGuide), which explicitly injects peptide–target interaction information into the generation process to steer peptide synthesis toward conformations that bind effectively to the target. Specifically, we introduce an interaction-aware network (IANet) to capture peptide–target interactions, and integrate the resulting interaction signal into the diffusion model via a time-dependent guidance injection strategy, thereby aligning the guidance with the diffusion trajectory. In addition, we propose a geometry-aware contrastive representation learning to enhance the separability between binder and non-binder samples. Experiments on comprehensive benchmarks show that PepGuide achieves state-of-the-art performance while demonstrating strong effectiveness and robust generalization across multiple binding pockets of a therapeutically relevant target.