[2510.08946] Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection

[2510.08946] Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection

arXiv - Machine Learning 4 min read

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Abstract page for arXiv paper 2510.08946: Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection

Quantitative Biology > Biomolecules arXiv:2510.08946 (q-bio) [Submitted on 10 Oct 2025 (v1), last revised 3 Mar 2026 (this version, v2)] Title:Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection Authors:Siyuan Chen, Minghao Guo, Caoliwen Wang, Anka He Chen, Yikun Zhang, Jingjing Chai, Yin Yang, Wojciech Matusik, Peter Yichen Chen View a PDF of the paper titled Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection, by Siyuan Chen and 8 other authors View PDF HTML (experimental) Abstract:Biomolecular interaction modeling has been substantially advanced by foundation models, yet they often produce all-atom structures that violate basic steric feasibility. We address this limitation by enforcing physical validity as a strict constraint during both training and inference with a uniffed module. At its core is a differentiable projection that maps the provisional atom coordinates from the diffusion model to the nearest physically valid conffguration. This projection is achieved using a Gauss-Seidel scheme, which exploits the locality and sparsity of the constraints to ensure stable and fast convergence at scale. By implicit differentiation to obtain gradients, our module integrates seamlessly into existing frameworks for end-to-end ffnetuning. With our Gauss-Seidel projection module in place, two denoising steps are sufffcient to produce biomolecular complexes that are both physically valid and structurally accurate. Across si...

Originally published on March 04, 2026. Curated by AI News.

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