[2510.14989] Constrained Diffusion for Protein Design with Hard Structural Constraints

[2510.14989] Constrained Diffusion for Protein Design with Hard Structural Constraints

arXiv - Machine Learning 3 min read

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Abstract page for arXiv paper 2510.14989: Constrained Diffusion for Protein Design with Hard Structural Constraints

Quantitative Biology > Biomolecules arXiv:2510.14989 (q-bio) [Submitted on 1 Oct 2025 (v1), last revised 25 Mar 2026 (this version, v2)] Title:Constrained Diffusion for Protein Design with Hard Structural Constraints Authors:Jacob K. Christopher, Austin Seamann, Jingyi Cui, Sagar Khare, Ferdinando Fioretto View a PDF of the paper titled Constrained Diffusion for Protein Design with Hard Structural Constraints, by Jacob K. Christopher and 4 other authors View PDF HTML (experimental) Abstract:Diffusion models offer a powerful means of capturing the manifold of realistic protein structures, enabling rapid design for protein engineering tasks. However, existing approaches observe critical failure modes when precise constraints are necessary for functional design. To this end, we present a constrained diffusion framework for structure-guided protein design, ensuring strict adherence to functional requirements while maintaining precise stereochemical and geometric feasibility. The approach integrates proximal feasibility updates with ADMM decomposition into the generative process, scaling effectively to the complex constraint sets of this domain. We evaluate on challenging protein design tasks, including motif scaffolding and vacancy-constrained pocket design, while introducing a novel curated benchmark dataset for motif scaffolding in the PDZ domain. Our approach achieves state-of-the-art, providing perfect satisfaction of bonding and geometric constraints with no degradation i...

Originally published on March 27, 2026. Curated by AI News.

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