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

Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a delicate balance between target compatibility, molecular properties, and physical geometry. While diffusion-based approaches have shown promise, strengthening the conditioning signal can still reduce physical plausibility, and existing models remain sensitive to how pocket information is presented during training and sampling. We propose PocketVE, a protein-pocket-conditioned variance-exploding (VE) diffusion framework that couples stable coordinate denoising with inference-time conditional control. Specifically, PocketVE combines an EDM-style training and sampling setup for stable 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 3D-Validity from 58.6 to 80.6 and reduces strain energy from 457.4 to 127.9 relative to the TAGMol baseline, while improving median Vina Score, QED, and SA under moderate guidance. A guidance-scale study further shows that moderate guidance gives the best balance between target-related objectives and geometric quality, whereas overly strong guidance pushes the sampler toward geometric degradation. Overall, the results show that target-aware molecular generation benefits from treating geometric stability and conditional steerability as coupled design goals.