PepARD: Full-Atom Peptide Design via Geometric Latent Diffusion with Adaptive Reward Steering

Target-aware peptide design requires simultaneously modeling molecular geometry, receptor interactions, and diverse biophysical constraints. However, existing methods often use coarse structural representations, separate peptide generation from pose prediction, or rely on fixed objectives during optimization. We propose PepARD, a unified framework for full-atom target-specific peptide design based on geometric latent diffusion. PepARD encodes peptide sequence and 3D structure into complementary invariant and $SE(3)$-equivariant latent spaces, while its Adaptive Multi-Channel $E(n)$-Equivariant Graph Neural Network (AMEGNN) captures detailed receptor-peptide interactions with variable heavy-atom configurations. Receptor-conditioned diffusion enables both de novo sequence-structure generation and fixed-sequence binding pose prediction within a shared framework. To further improve physical plausibility, an Adaptive Reward Weight Network dynamically balances structural, steric, geometric, and sequence objectives during latent refinement. Experiments on PepBench and PepBDB demonstrate that PepARD produces diverse and physically plausible peptides with favorable binding properties and improves recovery of native-like binding conformations.