Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 47 references that are in the map. First posted on arXiv 10 Oct 2025.
Builds on
12 of 47 references.
References · 12
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
- 2021Convex optimizationcited by 2,479
- 2023Directly Fine-Tuning Diffusion Models on Differentiable Rewardscited by 431
- 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 307
- 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problemcited by 229
- 2015Coordinate Descent Algorithmscited by 218
- 2022Riemannian Score-Based Generative Modellingcited by 216
- 2025A General Framework for Inference-time Scaling and Steering of Diffusion Modelscited by 201
- 2023Manifold Diffusion Fieldscited by 11
- 2024Vertex Block Descentcited by 5
Built on by
Citing papers per year: 2026: 3.
3 papers.
- 2026SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraintscited by 1
- 2026Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamicscited by 1
- 2026Differentiate the Solver, Not the Equation: Reverse-Sweep Adjoints for Block Implicit Simulationcited by 1
- 2026Differentiate the Solver, Not the Equation: Reverse-Sweep Adjoints for Block Implicit Simulation9 Aug 2026
- 2026Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics28 Apr 2026
- 2026SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints10 Feb 2026
Similar, not cited
Close in wording, no citation either way
- 2026Inference-Time Projection for Physically Valid Biomolecular Diffusion Models
- 2025Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models
- 2025Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
- 2026Few-step Cofolding with All-Atom Flow Maps
- 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
- 2026Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation
- 2024ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design
- 2025Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules versus Therapeutic Peptides