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

arXiv 2609.08101 ↗First posted 8 Sep 2026q-bio.BM cs.LGcited by 0

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Abstract (arXiv)

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, and adaptive protein perturbation as a training-time pocket regularizer.On the CrossDocked2020 benchmark under GenBench3D, PocketVE improves Valid3D from 58.6 to 80.6 and reduces strain energy from 457.4 to 127.9 relative to its guided TAGMol architectural parent; relative to TargetDiff, it attains comparable Valid3D with lower strain energy (127.9 vs. 306.0), 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 49 references that are in the map. First posted on arXiv 8 Sep 2026.

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33 of 49 references.

References · 33

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21 more
  1. 2022Structure-based Drug Design with Equivariant Diffusion Modelscited by 217
  2. 2023Geometric Latent Diffusion Models for 3D Molecule Generationcited by 186
  3. 2022Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pocketscited by 183
  4. 20233D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Predictioncited by 177
  5. 2022A 3D Generative Model for Structure-Based Drug Designcited by 149
  6. 2023MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generationcited by 98
  7. 2024DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Designcited by 88
  8. 2023Reflected Diffusion Modelscited by 87
  9. 2021Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Modelscited by 73
  10. 2024SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matchingcited by 41
  11. 2022Structure-based drug design with geometric deep learningcited by 40
  12. 2024Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimizationcited by 25
  13. 2024Tackling the Singularities at the Endpoints of Time Intervals in Diffusion Modelscited by 13
  14. 2024Binding-Adaptive Diffusion Models for Structure-Based Drug Designcited by 12
  15. 2024TAGMol: Target-Aware Gradient-guided Molecule Generationcited by 12
  16. 2025Improving Classifier-Free Guidance in Masked Diffusion: Low-Dim Theoretical Insights with High-Dim Impactcited by 11
  17. 2025GEOM-Drugs Revisited: Toward More Chemically Accurate Benchmarks for 3D Molecule Generationcited by 8
  18. 2025Prior-Guided Flow Matching for Target-Aware Molecule Design with Learnable Atom Numbercited by 6
  19. 2024General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Designcited by 5
  20. 2025Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generationcited by 4
  21. 2025Unraveling the Potential of Diffusion Models in Small Molecule Generationcited by 3

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