Literature DB >> 30103863

Automated techniques in pKa determination: Low, medium and high-throughput screening methods.

Christophe Dardonville1.   

Abstract

Drug discovery programs that generate hundreds of new molecular entities need efficient methodologies for physicochemical profiling. Several high-throughput methods for pKa screening have been developed in the last 15 years to determine this key physicochemical parameter. Separation techniques such as HPLC-MS or capillary electrophoresis are particularly well-suited due to their high throughput and capacity to deal with impure or complex samples. In addition, potentiometric and (mostly) UV-metric-based methods (plate-based and automated systems), find their place as very precise methodologies for pKa determination despite of somewhat lower throughput. Finally, pKa prediction software packages are useful estimator tools but, to date, they cannot replace experimental measurements when accurate pKa values are required.
Copyright © 2018 Elsevier Ltd. All rights reserved.

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Year:  2018        PMID: 30103863     DOI: 10.1016/j.ddtec.2018.04.001

Source DB:  PubMed          Journal:  Drug Discov Today Technol        ISSN: 1740-6749


  4 in total

1.  Efficient pKa Determination in a Nonaqueous Solvent Using Chemical Shift Imaging.

Authors:  George Schenck; Krzysztof Baj; Jonathan A Iggo; Matthew Wallace
Journal:  Anal Chem       Date:  2022-05-27       Impact factor: 8.008

2.  Critical Assessment of a Structure-Based Screening Campaign for IDO1 Inhibitors: Tips and Pitfalls.

Authors:  Andrea Mammoli; Elisa Bianconi; Luana Ruta; Alessandra Riccio; Carlo Bigiotti; Maria Souma; Andrea Carotti; Sofia Rossini; Chiara Suvieri; Maria Teresa Pallotta; Ursula Grohmann; Emidio Camaioni; Antonio Macchiarulo
Journal:  Int J Mol Sci       Date:  2022-04-02       Impact factor: 5.923

3.  PROTACs bearing piperazine-containing linkers: what effect on their protonation state?

Authors:  Jenny Desantis; Andrea Mammoli; Michela Eleuteri; Alice Coletti; Federico Croci; Antonio Macchiarulo; Laura Goracci
Journal:  RSC Adv       Date:  2022-08-09       Impact factor: 4.036

4.  Machine learning meets pK a.

Authors:  Marcel Baltruschat; Paul Czodrowski
Journal:  F1000Res       Date:  2020-02-13
  4 in total

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