11 papers
Asymmetric Flow Models
Hansheng Chen, Jan Ackermann, Minseo Kim +2
Flow-based generation in high-dimensional spaces is difficult because velocity prediction requires modeling high-dimensional noise, even when data has strong low-rank structure. We…
CrystalBoltz: End-to-End Protein Structure Determination via Experiment-Guided Diffusion for X-Ray Crystallography
Minseo Kim, Huanghao Mai, Jay Shenoy +3
Generative models trained on public databases of protein structures, most of which have been determined by X-ray crystallography, now provide powerful priors for structure predicti…
Dual Ascent Diffusion for Inverse Problems
Minseo Kim, Axel Levy, Gordon Wetzstein
Ill-posed inverse problems are fundamental in many domains, ranging from astrophysics to medical imaging. Emerging diffusion models provide a powerful prior for solving these probl…
Modeling Atomic Conformational Ensembles of Proteins via Test-Time Supervision of Boltz-2 on Cryo-EM Density Maps
Jay Shenoy, Miro Astore, Axel Levy +3
Knowledge of a protein's atomic conformational ensemble is critical to determining its function, yet state-of-the-art ensemble prediction models are limited by lack of high-quality…
Patch-Based Diffusion for Data-Efficient, Radiologist-Preferred MRI Reconstruction
Rohan Sanda, Asad Aali, Andrew Johnston +3
Magnetic resonance imaging (MRI) requires long acquisition times, raising costs, reducing accessibility, and making scans more susceptible to motion artifacts. Diffusion probabilis…
Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data
Rishwanth Raghu, Axel Levy, Gordon Wetzstein +1
Protein structure prediction models are now capable of generating accurate 3D structural hypotheses from sequence alone. However, they routinely fail to capture the conformational…