22 citations · 33 across the 5 of their papers we have counts for
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q-bio.QM2023★ 22 cited
Fast protein backbone generation with SE(3) flow matching
Jason Yim, Andrew Campbell, Andrew Y. K. Foong +8
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching. Specifically, we adapt FrameDiff, a state-of-the-art diffusion model, to the flow-mat…
cs.LG2023★ 3 cited
Reaction coordinate flows for model reduction of molecular kinetics
Hao Wu, Frank Noé
In this work, we introduce a flow based machine learning approach, called reaction coordinate (RC) flow, for discovery of low-dimensional kinetic models of molecular systems. The R…
cs.LG2023★ 7 cited
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
Tuan Le, Julian Cremer, Frank Noé +2
Deep generative diffusion models are a promising avenue for 3D de novo molecular design in materials science and drug discovery. However, their utility is still limited by suboptim…