3 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
Danny Reidenbach, Zhonglin Cao, Zuobai Zhang +8
High-quality training datasets are crucial for the development of effective protein design models, but existing synthetic datasets often include unfavorable sequence-structure pair…
Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow
Zhonglin Cao, Mario Geiger, Allan dos Santos Costa +6
Fast and accurate generation of molecular conformers is desired for downstream computational chemistry and drug discovery tasks. Currently, training and sampling state-of-the-art d…
La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching
Tomas Geffner, Kieran Didi, Zhonglin Cao +6
Recently, many generative models for de novo protein structure design have emerged. Yet, only few tackle the difficult task of directly generating fully atomistic structures jointl…
Learning Straight Flows by Learning Curved Interpolants
Shiv Shankar, Tomas Geffner
Flow matching models typically use linear interpolants to define the forward/noise addition process. This, together with the independent coupling between noise and target distribut…
ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids
Hannes Stark, Bowen Jing, Tomas Geffner +4
We develop ProtComposer to generate protein structures conditioned on spatial protein layouts that are specified via a set of 3D ellipsoids capturing substructure shapes and semant…
Proteina: Scaling Flow-based Protein Structure Generative Models
Tomas Geffner, Kieran Didi, Zuobai Zhang +8
Recently, diffusion- and flow-based generative models of protein structures have emerged as a powerful tool for de novo protein design. Here, we develop Proteina, a new large-scale…