Literature DB >> 21470178

Predicting the pKa of small molecule.

Matthias Rupp1, Robert Körner, Igor V Tetko.   

Abstract

The biopharmaceutical profile of a compound depends directly on the dissociation constants of its acidic and basic groups, commonly expressed as the negative decadic logarithm pKa of the acid dissociation constant (Ka). We survey the literature on computational methods to predict the pKa of small molecules. In this, we address data availability (used data sets, data quality, proprietary versus public data), molecular representations (quantum mechanics, descriptors, structured representations), prediction methods (approaches, implementations), as well as pKa-specific issues such as mono- and multiprotic compounds. We discuss advantages, problems, recent progress, and challenges in the field.

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Year:  2011        PMID: 21470178     DOI: 10.2174/138620711795508403

Source DB:  PubMed          Journal:  Comb Chem High Throughput Screen        ISSN: 1386-2073            Impact factor:   1.339


  14 in total

1.  Multi-task learning for pKa prediction.

Authors:  Grigorios Skolidis; Katja Hansen; Guido Sanguinetti; Matthias Rupp
Journal:  J Comput Aided Mol Des       Date:  2012-06-20       Impact factor: 3.686

2.  pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments.

Authors:  Mehtap Işık; Dorothy Levorse; Ariën S Rustenburg; Ikenna E Ndukwe; Heather Wang; Xiao Wang; Mikhail Reibarkh; Gary E Martin; Alexey A Makarov; David L Mobley; Timothy Rhodes; John D Chodera
Journal:  J Comput Aided Mol Des       Date:  2018-11-07       Impact factor: 3.686

3.  How Does the Methodology of 3D Structure Preparation Influence the Quality of pKa Prediction?

Authors:  Stanislav Geidl; Radka Svobodová Vařeková; Veronika Bendová; Lukáš Petrusek; Crina-Maria Ionescu; Zdeněk Jurka; Ruben Abagyan; Jaroslav Koča
Journal:  J Chem Inf Model       Date:  2015-06-11       Impact factor: 4.956

4.  Improving Small Molecule pK a Prediction Using Transfer Learning With Graph Neural Networks.

Authors:  Fritz Mayr; Marcus Wieder; Oliver Wieder; Thierry Langer
Journal:  Front Chem       Date:  2022-05-26       Impact factor: 5.545

5.  SAMPL6: calculation of macroscopic pKa values from ab initio quantum mechanical free energies.

Authors:  Edithe Selwa; Ian M Kenney; Oliver Beckstein; Bogdan I Iorga
Journal:  J Comput Aided Mol Des       Date:  2018-08-06       Impact factor: 3.686

6.  Enhancing Carbon Acid pKa Prediction by Augmentation of Sparse Experimental Datasets with Accurate AIBL (QM) Derived Values.

Authors:  Jeffrey Plante; Beth A Caine; Paul L A Popelier
Journal:  Molecules       Date:  2021-02-17       Impact factor: 4.411

7.  Predicting p Ka values from EEM atomic charges.

Authors:  Radka Svobodová Vařeková; Stanislav Geidl; Crina-Maria Ionescu; Ondřej Skřehota; Tomáš Bouchal; David Sehnal; Ruben Abagyan; Jaroslav Koča
Journal:  J Cheminform       Date:  2013-04-10       Impact factor: 5.514

8.  Impact of stereospecific intramolecular hydrogen bonding on cell permeability and physicochemical properties.

Authors:  Björn Over; Patrick McCarren; Per Artursson; Michael Foley; Fabrizio Giordanetto; Gunnar Grönberg; Constanze Hilgendorf; Maurice D Lee; Pär Matsson; Giovanni Muncipinto; Mélanie Pellisson; Matthew W D Perry; Richard Svensson; Jeremy R Duvall; Jan Kihlberg
Journal:  J Med Chem       Date:  2014-02-26       Impact factor: 7.446

9.  Opioid receptor signaling, analgesic and side effects induced by a computationally designed pH-dependent agonist.

Authors:  Viola Spahn; Giovanna Del Vecchio; Antonio Rodriguez-Gaztelumendi; Julia Temp; Dominika Labuz; Michael Kloner; Marco Reidelbach; Halina Machelska; Marcus Weber; Christoph Stein
Journal:  Sci Rep       Date:  2018-06-12       Impact factor: 4.379

10.  Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge.

Authors:  Teresa Danielle Bergazin; Nicolas Tielker; Yingying Zhang; Junjun Mao; M R Gunner; Karol Francisco; Carlo Ballatore; Stefan M Kast; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2021-06-24       Impact factor: 3.686

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