PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion
q-bio.BM, cs.LG
Submitted: 2026-09-08
Updated: 2026-09-27
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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.
Terminology
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 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 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.
Sources
- Benchmarking structure-based three-dimensional molecular generative models using GenBench3D: ligand conformation quality matters
- Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models?
- Classifier-Free Diffusion Guidance
- Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Number
- Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generation
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