What Limits Latent Diffusion for Receptor-Conditioned Peptide Design? From Latent-Space Quality to Interface Guidance

Receptor-conditioned peptide design requires sequences and all-atom conformations that fit a target protein interface. Existing methods, however, often rely on noisy latent sampling or uniform diffusion schedules that do not distinguish interface-critical residues from flexible regions. We introduce PepMALD, a manifold-anchored latent diffusion framework for interface-guided peptide design. Its Manifold-Anchored Hierarchical Autoencoder maps designable residues to deterministic latent states that capture local geometry, multi-level interactions, and complex-level context. Latent representation alignment and interface-guided adaptive diffusion then regulate denoising according to receptor proximity. This spatially adaptive process stabilizes interface residues earlier while preserving flexibility away from the binding surface. A dedicated Post-2026 set is designed to evaluate generalization to newly released receptor-peptide complexes. Across multiple benchmarks, PepMALD generates candidates with improved physical plausibility and interface compatibility. Mechanistic analyses associate these gains with a stable latent manifold and interface-first denoising, clarifying how receptor geometry guides residue-level generation. A colorectal cancer case study further indicates its potential for disease-relevant peptide design.