Search Results for author:
Found 8 papers, 8 papers with code
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
Our approach achieves state-of-the-art co-design performance while allowing the same multimodal model to be used for flexible generation of the sequence or structure.
Improved motif-scaffolding with SE(3) flow matching
The first is motif amortization, in which FrameFlow is trained with the motif as input using a data augmentation strategy.
Fast non-autoregressive inverse folding with discrete diffusion
Generating protein sequences that fold into a intended 3D structure is a fundamental step in de novo protein design.
Fast protein backbone generation with SE(3) flow matching
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching.
Improving Protein Optimization with Smoothed Fitness Landscapes
The ability to engineer novel proteins with higher fitness for a desired property would be revolutionary for biotechnology and medicine.
SE(3) diffusion model with application to protein backbone generation
The design of novel protein structures remains a challenge in protein engineering for applications across biomedicine and chemistry.
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes.
Sparse Projection Oblique Randomer Forests
Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency.
