22 citations · 33 across the 4 of their papers we have counts for
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cs.LG2026★ 1 cited
Generative Pseudo-Force Fields for Molecular Generation
Stefaan Simon Pierre Hessmann, Khaled Kahouli, Stefan Gugler +4
Generating stable molecular conformations typically forces a tradeoff between the physical realism of energy-based relaxation and the sampling efficiency of data-driven generative…
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…