PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion

2026-09-08

AI Summary

本文提出PocketVE，一种基于方差爆炸扩散框架的方法，用于解决蛋白质口袋条件下的3D分子生成问题，通过稳定坐标去噪和属性引导提高分子设计的有效性和兼容性。

Abstract

Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a balance between pocket compatibility, molecular properties, and physical geometry. We propose \textbf{PocketVE}, a protein-pocket-conditioned variance-exploding (VE) diffusion framework that couples stable coordinate denoising with inference-time property guidance. Specifically, PocketVE combines an EDM-style training and sampling setup for 3D denoising, classifier-free guidance for multi-property steering without external property classifiers, and adaptive protein perturbation as a training-time pocket regularizer. Evaluated on CrossDocked2020 under the GenBench3D protocol, PocketVE improves Valid$_{3\text{D}}$ from 58.6 to 80.6 and reduces strain energy from 457.4 to 127.9 relative to its TAGMol architectural baseline, while retaining competitive docking and molecular-property scores under moderate guidance. A guidance-scale study shows that moderate guidance gives a favorable balance between target-related objectives and geometric quality, whereas stronger guidance can degrade geometry and distributional fidelity. Pocket-permutation and PoseCheck diagnostics further support pocket-specific spatial compatibility with reduced steric conflicts. Overall, the results suggest that geometric stability and inference-time property guidance should be considered as coupled design objectives.

Problem

• Protein-conditioned
• 3D molecule generation
• structure-based drug design
• pocket compatibility
• molecular properties

Innovation

• Variance-Exploding Diffusion
• Classifier-Free Guidance
• Adaptive Protein Perturbation