High accuracy quantum-chemistry-based calculation and blind prediction of macroscopic pKa values in the context of the SAMPL6 challenge

Recent advances in the development of low-cost quantum chemical methods have made the prediction of conformational preferences and physicochemical properties of medium-sized drug-like molecules routinely feasible, with significant potential to advance drug discovery. In the context of the SAMPL6 cha...

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Veröffentlicht in:Journal of computer-aided molecular design 2018-10, Vol.32 (10), p.1139-1149
Hauptverfasser: Pracht, Philipp, Wilcken, Rainer, Udvarhelyi, Anikó, Rodde, Stephane, Grimme, Stefan
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creator Pracht, Philipp
Wilcken, Rainer
Udvarhelyi, Anikó
Rodde, Stephane
Grimme, Stefan
description Recent advances in the development of low-cost quantum chemical methods have made the prediction of conformational preferences and physicochemical properties of medium-sized drug-like molecules routinely feasible, with significant potential to advance drug discovery. In the context of the SAMPL6 challenge, macroscopic pKa values were blindly predicted for a set of 24 of such molecules. In this paper we present two similar quantum chemical based approaches based on the high accuracy calculation of standard reaction free energies and the subsequent determination of those pKa values via a linear free energy relationship. Both approaches use extensive conformational sampling and apply hybrid and double-hybrid density functional theory with continuum solvation to calculate free energies. The blindly calculated macroscopic pKa values were in excellent agreement with the experiment.
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subjects Animal Anatomy
Chemistry
Chemistry and Materials Science
Computer Applications in Chemistry
Density functional theory
Free energy
Histology
Mathematical analysis
Morphology
Organic chemistry
Physical Chemistry
Physicochemical properties
Predictions
Quantum chemistry
Solvation
Sorption
title High accuracy quantum-chemistry-based calculation and blind prediction of macroscopic pKa values in the context of the SAMPL6 challenge
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