Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915829482782720 |
|---|---|
| author | Chen, Siyuan Guo, Minghao Wang, Caoliwen Chen, Anka He Zhang, Yikun Chai, Jingjing Yang, Yin Matusik, Wojciech Chen, Peter Yichen |
| author_facet | Chen, Siyuan Guo, Minghao Wang, Caoliwen Chen, Anka He Zhang, Yikun Chai, Jingjing Yang, Yin Matusik, Wojciech Chen, Peter Yichen |
| contents | 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 six benchmarks, our 2-step model achieves the same structural accuracy as state-of-the-art 200-step diffusion baselines, delivering approximately 10 times faster wall-clock speed while guaranteeing physical validity. The code is available at https://github.com/chensiyuan030105/ProteinGS.git. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08946 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection Chen, Siyuan Guo, Minghao Wang, Caoliwen Chen, Anka He Zhang, Yikun Chai, Jingjing Yang, Yin Matusik, Wojciech Chen, Peter Yichen Biomolecules Machine Learning 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 six benchmarks, our 2-step model achieves the same structural accuracy as state-of-the-art 200-step diffusion baselines, delivering approximately 10 times faster wall-clock speed while guaranteeing physical validity. The code is available at https://github.com/chensiyuan030105/ProteinGS.git. |
| title | Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2510.08946 |