198 citations · 435 across the 6 of their papers we have counts for
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q-bio.BM2023★ 6 cited
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…
physics.chem-ph2023★ 3 cited
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics
Andreas Krämer, Aleksander P. Durumeric, Nicholas E. Charron +3
Machine-learned coarse-grained (CG) models have the potential for simulating large molecular complexes beyond what is possible with atomistic molecular dynamics. However, training…