72 citations · 262 across the 18 of their papers we have counts for
4 papers · 1 filter
Evaluating representation learning on the protein structure universe
Arian R. Jamasb, Alex Morehead, Chaitanya K. Joshi +8
We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural Networks. We consider large-scale pre-tr…
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design
Rishabh Anand, Chaitanya K. Joshi, Alex Morehead +7
We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone design. We build upon SE(3) flow matching for protein backbone generation and establish protocols for dat…
Improving Antibody Design with Force-Guided Sampling in Diffusion Models
Paulina Kulytė, Francisco Vargas, Simon Valentin Mathis +3
Antibodies, crucial for immune defense, primarily rely on complementarity-determining regions (CDRs) to bind and neutralize antigens, such as viruses. The design of these CDRs dete…
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised -transform
Alexander Denker, Francisco Vargas, Shreyas Padhy +7
Generative modelling paradigms based on denoising diffusion processes have emerged as a leading candidate for conditional sampling in inverse problems. In many real-world applicati…