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Prediction of n-octanol/water partition coefficients and acidity constants (pK(a)) in the SAMPL7 blind challenge with the IEFPCM-MST model

Within the scope of SAMPL7 challenge for predicting physical properties, the Integral Equation Formalism of the Miertus-Scrocco-Tomasi (IEFPCM/MST) continuum solvation model has been used for the blind prediction of n-octanol/water partition coefficients and acidity constants of a set of 22 and 20 s...

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Autores principales: Viayna, Antonio, Pinheiro, Silvana, Curutchet, Carles, Luque, F. Javier, Zamora, William J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295120/
https://www.ncbi.nlm.nih.gov/pubmed/34244905
http://dx.doi.org/10.1007/s10822-021-00394-6
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author Viayna, Antonio
Pinheiro, Silvana
Curutchet, Carles
Luque, F. Javier
Zamora, William J.
author_facet Viayna, Antonio
Pinheiro, Silvana
Curutchet, Carles
Luque, F. Javier
Zamora, William J.
author_sort Viayna, Antonio
collection PubMed
description Within the scope of SAMPL7 challenge for predicting physical properties, the Integral Equation Formalism of the Miertus-Scrocco-Tomasi (IEFPCM/MST) continuum solvation model has been used for the blind prediction of n-octanol/water partition coefficients and acidity constants of a set of 22 and 20 sulfonamide-containing compounds, respectively. The log P and pK(a) were computed using the B3LPYP/6-31G(d) parametrized version of the IEFPCM/MST model. The performance of our method for partition coefficients yielded a root-mean square error of 1.03 (log P units), placing this method among the most accurate theoretical approaches in the comparison with both globally (rank 8th) and physical (rank 2nd) methods. On the other hand, the deviation between predicted and experimental pK(a) values was 1.32 log units, obtaining the second best-ranked submission. Though this highlights the reliability of the IEFPCM/MST model for predicting the partitioning and the acid dissociation constant of drug-like compounds compound, the results are discussed to identify potential weaknesses and improve the performance of the method. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10822-021-00394-6.
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spelling pubmed-82951202021-07-23 Prediction of n-octanol/water partition coefficients and acidity constants (pK(a)) in the SAMPL7 blind challenge with the IEFPCM-MST model Viayna, Antonio Pinheiro, Silvana Curutchet, Carles Luque, F. Javier Zamora, William J. J Comput Aided Mol Des Article Within the scope of SAMPL7 challenge for predicting physical properties, the Integral Equation Formalism of the Miertus-Scrocco-Tomasi (IEFPCM/MST) continuum solvation model has been used for the blind prediction of n-octanol/water partition coefficients and acidity constants of a set of 22 and 20 sulfonamide-containing compounds, respectively. The log P and pK(a) were computed using the B3LPYP/6-31G(d) parametrized version of the IEFPCM/MST model. The performance of our method for partition coefficients yielded a root-mean square error of 1.03 (log P units), placing this method among the most accurate theoretical approaches in the comparison with both globally (rank 8th) and physical (rank 2nd) methods. On the other hand, the deviation between predicted and experimental pK(a) values was 1.32 log units, obtaining the second best-ranked submission. Though this highlights the reliability of the IEFPCM/MST model for predicting the partitioning and the acid dissociation constant of drug-like compounds compound, the results are discussed to identify potential weaknesses and improve the performance of the method. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10822-021-00394-6. Springer International Publishing 2021-07-10 2021 /pmc/articles/PMC8295120/ /pubmed/34244905 http://dx.doi.org/10.1007/s10822-021-00394-6 Text en © The Author(s) 2021, corrected publication 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Viayna, Antonio
Pinheiro, Silvana
Curutchet, Carles
Luque, F. Javier
Zamora, William J.
Prediction of n-octanol/water partition coefficients and acidity constants (pK(a)) in the SAMPL7 blind challenge with the IEFPCM-MST model
title Prediction of n-octanol/water partition coefficients and acidity constants (pK(a)) in the SAMPL7 blind challenge with the IEFPCM-MST model
title_full Prediction of n-octanol/water partition coefficients and acidity constants (pK(a)) in the SAMPL7 blind challenge with the IEFPCM-MST model
title_fullStr Prediction of n-octanol/water partition coefficients and acidity constants (pK(a)) in the SAMPL7 blind challenge with the IEFPCM-MST model
title_full_unstemmed Prediction of n-octanol/water partition coefficients and acidity constants (pK(a)) in the SAMPL7 blind challenge with the IEFPCM-MST model
title_short Prediction of n-octanol/water partition coefficients and acidity constants (pK(a)) in the SAMPL7 blind challenge with the IEFPCM-MST model
title_sort prediction of n-octanol/water partition coefficients and acidity constants (pk(a)) in the sampl7 blind challenge with the iefpcm-mst model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295120/
https://www.ncbi.nlm.nih.gov/pubmed/34244905
http://dx.doi.org/10.1007/s10822-021-00394-6
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