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SAMPL7 physical property prediction from EC-RISM theory
Inspired by the successful application of the embedded cluster reference interaction site model (EC-RISM), a combination of quantum–mechanical calculations with three-dimensional RISM theory to predict Gibbs energies of species in solution within the SAMPL6.1 (acidity constants, pK(a)) and SAMPL6.2...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367877/ https://www.ncbi.nlm.nih.gov/pubmed/34278539 http://dx.doi.org/10.1007/s10822-021-00410-9 |
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author | Tielker, Nicolas Güssregen, Stefan Kast, Stefan M. |
author_facet | Tielker, Nicolas Güssregen, Stefan Kast, Stefan M. |
author_sort | Tielker, Nicolas |
collection | PubMed |
description | Inspired by the successful application of the embedded cluster reference interaction site model (EC-RISM), a combination of quantum–mechanical calculations with three-dimensional RISM theory to predict Gibbs energies of species in solution within the SAMPL6.1 (acidity constants, pK(a)) and SAMPL6.2 (octanol–water partition coefficients, log P) the methodology was applied to the recent SAMPL7 physical property challenge on aqueous pK(a) and octanol–water log P values. Not part of the challenge but provided by the organizers, we also computed distribution coefficients log D(7.4) from predicted pK(a) and log P data. While macroscopic pK(a) predictions compared very favorably with experimental data (root mean square error, RMSE 0.72 pK units), the performance of the log P model (RMSE 1.84) fell behind expectations from the SAMPL6.2 challenge, leading to reasonable log D(7.4) predictions (RMSE 1.69) from combining the independent calculations. In the post-submission phase, conformations generated by different methodology yielded results that did not significantly improve the original predictions. While overall satisfactory compared to previous log D challenges, the predicted data suggest that further effort is needed for optimizing the robustness of the partition coefficient model within EC-RISM calculations and for shaping the agreement between experimental conditions and the corresponding model description. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10822-021-00410-9. |
format | Online Article Text |
id | pubmed-8367877 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-83678772021-08-31 SAMPL7 physical property prediction from EC-RISM theory Tielker, Nicolas Güssregen, Stefan Kast, Stefan M. J Comput Aided Mol Des Article Inspired by the successful application of the embedded cluster reference interaction site model (EC-RISM), a combination of quantum–mechanical calculations with three-dimensional RISM theory to predict Gibbs energies of species in solution within the SAMPL6.1 (acidity constants, pK(a)) and SAMPL6.2 (octanol–water partition coefficients, log P) the methodology was applied to the recent SAMPL7 physical property challenge on aqueous pK(a) and octanol–water log P values. Not part of the challenge but provided by the organizers, we also computed distribution coefficients log D(7.4) from predicted pK(a) and log P data. While macroscopic pK(a) predictions compared very favorably with experimental data (root mean square error, RMSE 0.72 pK units), the performance of the log P model (RMSE 1.84) fell behind expectations from the SAMPL6.2 challenge, leading to reasonable log D(7.4) predictions (RMSE 1.69) from combining the independent calculations. In the post-submission phase, conformations generated by different methodology yielded results that did not significantly improve the original predictions. While overall satisfactory compared to previous log D challenges, the predicted data suggest that further effort is needed for optimizing the robustness of the partition coefficient model within EC-RISM calculations and for shaping the agreement between experimental conditions and the corresponding model description. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10822-021-00410-9. Springer International Publishing 2021-07-19 2021 /pmc/articles/PMC8367877/ /pubmed/34278539 http://dx.doi.org/10.1007/s10822-021-00410-9 Text en © The Author(s) 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 Tielker, Nicolas Güssregen, Stefan Kast, Stefan M. SAMPL7 physical property prediction from EC-RISM theory |
title | SAMPL7 physical property prediction from EC-RISM theory |
title_full | SAMPL7 physical property prediction from EC-RISM theory |
title_fullStr | SAMPL7 physical property prediction from EC-RISM theory |
title_full_unstemmed | SAMPL7 physical property prediction from EC-RISM theory |
title_short | SAMPL7 physical property prediction from EC-RISM theory |
title_sort | sampl7 physical property prediction from ec-rism theory |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8367877/ https://www.ncbi.nlm.nih.gov/pubmed/34278539 http://dx.doi.org/10.1007/s10822-021-00410-9 |
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