arXiv cs.AI· Qurat-ul-ain, Yee Whye Teh, Charlotte M. Deane, Matteo Cagiada·· 10 天前AI 评分30
面向物理有效生物分子扩散模型的推理时投影方法
Inference-Time Projection for Physically Valid Biomolecular Diffusion Models
AI 导读
针对 AlphaFold 3 风格共折叠模型输出常出现链重叠、配体键长键角畸变、环非平面和手性反转等物理无效问题，研究者提出两种闭式投影算子，作用于扩散模型去噪后的干净坐标估计 x̂₀：链间范德华投影推开严重碰撞的原子对，配体距离几何投影恢复键长、键角、内部接触、平面性和手性。
正文 · 原文
Abstract:AlphaFold 3-style cofolding models predict biomolecular complexes with high structural accuracy, yet a large fraction of their outputs are physically invalid: chains overlap at interfaces, ligand bond lengths and angles are distorted, rings are non-planar, and stereocentres are inverted. Current approaches either steer the sampler with physics-informed potentials, which multiplies sampling cost and memory overhead making inference impossible on large complexes, or finetune the model, costing time and tying the fix to one architecture. We observe that, unlike structural accuracy, physical validity is fully verifiable at inference time from quantities the sampler already holds. We therefore treat physical validity as a constrained inference problem and introduce two closed-form projection operators applied to the diffusion model's denoised clean-coordinate estimate, $\hat{x}_0$: an inter-chain van der Waals projection that pushes apart the most severely clashing atom pairs, and a ligand distance-geometry projection that restores bond lengths, angles, internal contacts, planarity and chirality. Both operators are local, sparse and displacement-capped, require no network evaluations, gradients or importance sampling, and leave the denoiser and its weights untouched, so they can be dropped into any AF3-style sampler without retraining. Applied to two independently developed models, Boltz-2 and OpenFold-3, across five benchmarks (CASP15, CASP16, the PoseBusters monomer and complex sets, and the Boltz physical-validity test set), our method recovers perfect physical validity while preserving structural-accuracy and ligand-placement metrics. These gains are achieved with negligible runtime and memory overhead, providing a practical, model-agnostic route to physically valid all-atom structure prediction.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.07037 [cs.AI] |
| (or arXiv:2610.07037v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07037 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Q Qurat-Ul-Ain [view email]
[v1]
Sun, 4 Oct 2026 23:15:03 UTC (12,299 KB)
来源：arXiv cs.AI · arxiv.org