Surprisal Metrics for Quantifying Perturbed Conformational Dynamics in Markov State Models
Vincent A. Voelz(Temple University), Guangfeng Zhou(University of Washington), B. Elman(Temple University), Asghar M. Razavi(Temple University)
Cited by 27
Related Papers
SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome
|Nature Chemistry|2021|301
An artificial intelligence accelerated virtual screening platform for drug discovery
|Nature Communications|2024|189
Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein–Ligand Docking
|Journal of Chemical Theory and Computation|2021|112
Bridging Microscopic and Macroscopic Mechanisms of p53-MDM2 Binding with Kinetic Network Models
|Biophysical Journal|2017|95
Cyclic peptide structure prediction and design using AlphaFold
|bioRxiv (Cold Spring Harbor Laboratory)|2023|80