no code implementations • 16 Feb 2024 • Peter Eckmann, Dongxia Wu, Germano Heinzelmann, Michael K Gilson, Rose Yu
Current generative models for drug discovery primarily use molecular docking to evaluate the quality of generated compounds.
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.
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.