Generalized Peptide Design with Physically Inspired Diffusion

Peptides, as short amino acid polymers, have attracted increasing attention due to their favorable pharmacological properties. Recent advances in deep learning have greatly facilitated the design and discovery of peptides. However, most existing generative approaches rely primarily on static structural representations, and the generated peptides generally adhere closely to patterns in the training data, which inherently limits the exploration of functionally diverse conformations and weakens generalization capability to unseen scenarios. Inspired by the dynamic physical process of molecular docking, where hotspot regions form key interactions and flexible regions enable adaptive fit, we propose two complementary strategies for peptide generation: (i) **Flexibility-Conditioned Out-of-Distribution Exploration**, which encourages the model to dynamically explore the conformational landscape based on structural flexibility, thereby promoting high novelty; and (ii) **Hotspot-Driven Hierarchical Affinity Guidance**, which steers the exploration process toward high-affinity binding modes by prioritizing key interactions at hotspot regions. Integrating these strategies, we introduce **PhyDiff**, a physically informed diffusion-based framework that adaptively explores the chemical space while simultaneously discovering high-affinity binding patterns, thereby producing peptides that exhibit both strong binding affinity and novelty, and ultimately enhancing the model's generalization capability in real-world drug design scenarios. Extensive experiments across diverse peptide design benchmarks consistently demonstrate the effectiveness and generalization capability of our approach.