no code implementations • 27 Nov 2023 • Peter Eckmann, Jake Anderson, Michael K. Gilson, Rose Yu
Predicting the activities of compounds against protein-based or phenotypic assays using only a few known compounds and their activities is a common task in target-free drug discovery.
no code implementations • 2 Mar 2023 • Chapin E. Cavender, David A. Case, Julian C. -H. Chen, Lillian T. Chong, Daniel A. Keedy, Kresten Lindorff-Larsen, David L. Mobley, O. H. Samuli Ollila, Chris Oostenbrink, Paul Robustelli, Vincent A. Voelz, Michael E. Wall, David C. Wych, Michael K. Gilson
This review article provides an overview of structurally oriented, experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear magnetic resonance (NMR) spectroscopy and room temperature (RT) protein crystallography.
1 code implementation • 17 Jun 2022 • Peter Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, Michael K. Gilson, Rose Yu
We corroborate these docking-based results with more accurate molecular dynamics-based calculations of absolute binding free energy and show that one of our generated drug-like compounds has a predicted $K_D$ (a measure of binding affinity) of $6 \cdot 10^{-14}$ M against the human estrogen receptor, well beyond the affinities of typical early-stage drug candidates and most FDA-approved drugs to their respective targets.