23 citations · 54 across the 14 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…
Flows, straight but not so fast: Exploring the design space of Rectified Flows in Protein Design
Junhua Chen, Simon Mathis, Charles Harris +2
Generative modeling techniques such as Diffusion and Flow Matching have achieved significant successes in generating designable and diverse protein backbones. However, many current…
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…
Compositional Flows for 3D Molecule and Synthesis Pathway Co-design
Tony Shen, Seonghwan Seo, Ross Irwin +4
Many generative applications, such as synthesis-based 3D molecular design, involve constructing compositional objects with continuous features. Here, we introduce Compositional Gen…
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…
MotifBench: A standardized protein design benchmark for motif-scaffolding problems
Zhuoqi Zheng, Bo Zhang, Kieran Didi +5
The motif-scaffolding problem is a central task in computational protein design: Given the coordinates of atoms in a geometry chosen to confer a desired biochemical function (a mot…