Confidence-Based Diffusion Sampling with Geometric Readiness Awareness for Accelerated Structure-based Drug Design

Structure-based drug design with diffusion models seeks to generate 3D molecules that bind tightly to target protein pockets, but full-step sampling is too slow for high-throughput use. Extending the recent development of training-free fast sampling methods for diffusion models (e.g., DDIM and DNDM) to high-quality 3D molecule generation is non-trivial, given that both discrete variables (atom types) and continuous variables (atom coordinates) are involved. In this paper, we propose a unified fast sampling framework that allows a shared time schedule across discrete and continuous variables for strategically accelerating the 3D ligand generation. Guided by an error-equalization principle, we combine the continuous and discrete error rates of hybrid sampling into a single step-importance density and derive a training-free coupling-aware schedule. We further propose a confidence-guided sampling strategy that integrates constraints of model prediction confidence, motion stability, and steric clash to control the acceleration based on geometric readiness. Extensive experiments demonstrate that our method achieves $18$-$20\times$ speedup compared to full-step diffusion models while preserving state-of-the-art generation quality across chemical properties, binding affinity, and chemical validity.