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Polarizable embedding combined with the algebraic diagrammatic construction: tackling excited states in biomolecular systems

Drug-target residence time (t), one of the main determinants of drug efficacy, remains highly challenging to predict computationally and, therefore, is usually not considered in the early stages of drug design. Here, we present an efficient computational method, t-random acceleration molecular dynam...

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Hauptverfasser: Scheurer, Maximilian (VerfasserIn) , Herbst, Michael F. (VerfasserIn) , Dreuw, Andreas (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: August 7, 2018
In: Journal of chemical theory and computation
Year: 2018, Jahrgang: 14, Heft: 9, Pages: 4870-7883
ISSN:1549-9626
DOI:10.1021/acs.jctc.8b00576
Online-Zugang:Verlag, Volltext: https://doi.org/10.1021/acs.jctc.8b00576
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Verfasserangaben:Maximilian Scheurer, Michael F. Herbst, Peter Reinholdt, Jógvan Magnus Haugaard Olsen, Andreas Dreuw, and Jacob Kongsted
Beschreibung
Zusammenfassung:Drug-target residence time (t), one of the main determinants of drug efficacy, remains highly challenging to predict computationally and, therefore, is usually not considered in the early stages of drug design. Here, we present an efficient computational method, t-random acceleration molecular dynamics (tRAMD), for the ranking of drug candidates by their residence time and obtaining insights into ligand-target dissociation mechanisms. We assessed tRAMD on a data set of 70 diverse drug-like ligands of the N-terminal domain of HSP90α, a pharmaceutically important target with a highly flexible binding site, obtaining computed relative residence times with an accuracy of about 2.3τ for 78% of the compounds and less than 2.0τ within congeneric series. Analysis of dissociation trajectories reveals features that affect ligand unbinding rates, including transient polar interactions and steric hindrance. These results suggest that tRAMD will be widely applicable as a computationally efficient aid to improving drug residence times during lead optimization.
Beschreibung:Published: August 7, 2018
Gesehen am 27.06.2019
Beschreibung:Online Resource
ISSN:1549-9626
DOI:10.1021/acs.jctc.8b00576

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