10 citations · 38 across the 6 of their papers we have counts for
6 papers
Transferable Boltzmann Generators
Leon Klein, Frank Noé
The generation of equilibrium samples of molecular systems has been a long-standing problem in statistical physics. Boltzmann Generators are a generative machine learning method th…
PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling
Julian Cremer, Tuan Le, Frank Noé +2
The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design. H…
An improved penalty-based excited-state variational Monte Carlo approach with deep-learning ansatzes
P. Bernát Szabó, Zeno Schätzle, Mike T. Entwistle +1
We introduce several improvements to the penalty-based variational quantum Monte Carlo (VMC) algorithm for computing electronic excited states of Entwistle [M. T.…
Dicke superradiant enhancement of the heat current in circuit QED
Gian Marcello Andolina, Paolo Andrea Erdman, Frank Noé +2
Collective effects, such as Dicke superradiant emission, can enhance the performance of a quantum device. Here, we study the heat current flowing between a cold and a hot bath thro…
Improved motif-scaffolding with SE(3) flow matching
Jason Yim, Andrew Campbell, Emile Mathieu +9
Protein design often begins with the knowledge of a desired function from a motif which motif-scaffolding aims to construct a functional protein around. Recently, generative models…
Navigating protein landscapes with a machine-learned transferable coarse-grained model
Nicholas E. Charron, Felix Musil, Andrea Guljas +18
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a uni…