Search Results - "Stein, Reed"
-
1
A practical guide to large-scale docking
Published in Nature protocols (01-10-2021)“…Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and…”
Get full text
Journal Article -
2
Virtual discovery of melatonin receptor ligands to modulate circadian rhythms
Published in Nature (London) (01-03-2020)“…The neuromodulator melatonin synchronizes circadian rhythms and related physiological functions through the actions of two G-protein-coupled receptors: MT 1…”
Get full text
Journal Article -
3
Property-Unmatched Decoys in Docking Benchmarks
Published in Journal of chemical information and modeling (22-02-2021)“…Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of…”
Get full text
Journal Article -
4
Testing inhomogeneous solvation theory in structure-based ligand discovery
Published in Proceedings of the National Academy of Sciences - PNAS (15-08-2017)“…Binding-site water is often displaced upon ligand recognition, but is commonly neglected in structure-based ligand discovery. Inhomogeneous solvation theory…”
Get full text
Journal Article -
5
Publisher Correction: A practical guide to large-scale docking
Published in Nature protocols (2022)Get full text
Journal Article -
6
In vivo Efficacy of Novel Type Preferring MT 1 Melatonin Receptor Inverse Agonists in C3H/HeN Mouse Models of Chronobiological Behavior
Published in The FASEB journal (01-04-2020)“…Abstract only Drug discovery in the melatonin field has been hindered by lack of selective MT 1 and MT 2 melatonin receptors ligands with in vivo receptor type…”
Get full text
Journal Article -
7
Understanding Virtual Solvent Through Large-Scale Ligand Discovery
Published 01-01-2020“…Predicting new ligands and their binding poses for a protein target relies on an understanding of the physical forces that exist between the water-submerged…”
Get full text
Dissertation -
8
Abstract 5333: Combining chemoproteomics with machine learning identifies functionally active covalent fragments for hard-to-drug cancer drivers
Published in Cancer research (Chicago, Ill.) (04-04-2023)“…Abstract a) Many cancer drivers are considered “undruggable” and without targeted treatments because they lack binding sites for conventional small molecules…”
Get full text
Journal Article