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SE(3) diffusion model with application to protein backbone generation
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The design of novel protein structures remains a challenge in protein engineering for applications across biomedicine and chemistry. In this line of work, a diffusion model over rigid bodies in 3D (referred to as frames) has shown success in generating novel, functional protein backbones that have not been observed in nature. However, there exists no principled methodological framework for diffusion on SE(3), the space of orientation preserving rigid motions in R3, that operates on frames and confers the group invariance. We address these shortcomings by developing theoretical foundations of SE(3) invariant diffusion models on multiple frames followed by a novel framework, FrameDiff, for learning the SE(3) equivariant score over multiple frames. We apply FrameDiff on monomer backbone generation and find it can generate designable monomers up to 500 amino acids without relying on a pretrained protein structure prediction network that has been integral to previous methods. We find our samples are capable of generalizing beyond any known protein structure.
Forward citations
Cited by 8 Pith papers
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Spectral Diffusion for Protein Dynamics
Diffusion over DCT spectral volumes of Cα displacements yields fast, temperature-conditioned protein trajectories with RMSF Pearson r of 0.844 on held-out mdCATH.
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GraspMeanFlow: SE(3)-Equivariant MeanFlow for Few-Step 6-DoF Grasp Generation
An SE(3)-equivariant average-velocity flow generates 6-DoF grasps in one or a few function evaluations, matching iterative flow baselines on ACRONYM.
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SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups
SE(3)-MeanFlow trains a protein backbone generator to predict average Lie-group velocities, reaching comparable designability to flow matching at 20–100 steps and leading at 10 steps after rectification.
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FMOPF: Latent Flow Matching with Constraint-Aware Interaction Priors for AC Optimal Power Flow
FMOPF uses latent flow matching plus a constraint-aware interaction prior to sample feasible near-optimal AC-OPF solutions, and reports the first generative-OPF scaling to 300 buses — but its feasibility claim is not ...
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Exploring the Alignment of Generation and Understanding in Protein Structure Modeling
Aligning a protein diffusion generator's internal representations to a pretrained structure encoder (ProteinMPNN) raises the MotifBench motif-scaffolding score from 39.2 to 47.1 (~20% relative) over the Protpardelle-1...
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Variable-Length Generative Protein Design via Generalized Poisson Flow
Generalized Poisson Flow learns variable protein length via an inhomogeneous Poisson rate plus within-length flow matching, with KL bounds and gains on structure, sequence, motif, and peptide tasks.
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Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints
CP-Composer trains a geometric diffusion model on linear peptides and imposes cyclization constraints at generation time, achieving 38-84% constraint satisfaction across four cyclization strategies.
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Energy-Based Flow Matching for Generating 3D Molecular Structure
IDFlow trains a flow matching network to refine its own predicted 3D molecular structure, improving docking and protein backbone generation over HarmonicFlow and FrameFlow baselines.
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