GB-FESO backpropagates a KL-divergence loss through a frozen conditional diffusion model's sampling trajectory to optimize system parameters so the generated ensemble matches a target free-energy surface.
Eigenfold: Generative protein structure prediction with diffusion models
5 Pith papers cite this work, alongside 39 external citations. Polarity classification is still indexing.
5
Pith papers citing it
39
external citations · Pith
abstract
Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function. Towards this goal, we develop EigenFold, a diffusion generative modeling framework for sampling a distribution of structures from a given protein sequence. We define a diffusion process that models the structure as a system of harmonic oscillators and which naturally induces a cascading-resolution generative process along the eigenmodes of the system. On recent CAMEO targets, EigenFold achieves a median TMScore of 0.84, while providing a more comprehensive picture of model uncertainty via the ensemble of sampled structures relative to existing methods. We then assess EigenFold's ability to model and predict conformational heterogeneity for fold-switching proteins and ligand-induced conformational change. Code is available at https://github.com/bjing2016/EigenFold.
representative citing papers
GLENS uses diffusion models on solver iterates to generate high-quality and diverse initial guesses for multimodal non-convex optimization, leading to faster solver convergence.
Clari, a unit-cell flow matching model with pair-bias attention, generates organic crystal structures faster than OXtal while improving solve rates and supporting energy-based ranking without relaxation.
ARCS generates valid SPICE-simulatable analog circuits in milliseconds via graph VAE, flow-matching, and GRPO reinforcement learning, reaching 99.9% validity with 8 evaluations across 32 topologies.
EnFlow integrates flow-based conformer generation with energy landscape modeling to enable joint ensemble generation and ground-state identification using only 1-2 ODE steps.
citing papers explorer
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Gradient-Based Inverse Design of Free-Energy Landscapes with Diffusion Models
GB-FESO backpropagates a KL-divergence loss through a frozen conditional diffusion model's sampling trajectory to optimize system parameters so the generated ensemble matches a target free-energy surface.
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GLENS: Global Search via Learning from Solver Iterates with Diffusion Models
GLENS uses diffusion models on solver iterates to generate high-quality and diverse initial guesses for multimodal non-convex optimization, leading to faster solver convergence.
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Fast Organic Crystal Structure Prediction with Unit Cell Flow Matching
Clari, a unit-cell flow matching model with pair-bias attention, generates organic crystal structures faster than OXtal while improving solve rates and supporting energy-based ranking without relaxation.
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ARCS: Autoregressive Circuit Synthesis with Topology-Aware Graph Attention and Spec Conditioning
ARCS generates valid SPICE-simulatable analog circuits in milliseconds via graph VAE, flow-matching, and GRPO reinforcement learning, reaching 99.9% validity with 8 evaluations across 32 topologies.
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Energy-Guided Generative Modeling for Low-Energy Molecular Structure Discovery
EnFlow integrates flow-based conformer generation with energy landscape modeling to enable joint ensemble generation and ground-state identification using only 1-2 ODE steps.