DMSO Solubility Assessment for Fragment-Based Screening

In this paper, we report comprehensive experimental and chemoinformatics analyses of the solubility of small organic molecules (“fragments”) in dimethyl sulfoxide (DMSO) in the context of their ability to be tested in screening experiments. Here, DMSO solubility of 939 fragments has been measured ex...

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Main Authors: Shamkhal Baybekov, Gilles Marcou, Pascal Ramos, Olivier Saurel, Jean-Luc Galzi, Alexandre Varnek
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Molecules
Subjects:
NMR
Online Access:https://www.mdpi.com/1420-3049/26/13/3950
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spelling doaj-d4efb96a2a7946e986453cb49f8ec3992021-07-15T15:42:31ZengMDPI AGMolecules1420-30492021-06-01263950395010.3390/molecules26133950DMSO Solubility Assessment for Fragment-Based ScreeningShamkhal Baybekov0Gilles Marcou1Pascal Ramos2Olivier Saurel3Jean-Luc Galzi4Alexandre Varnek5Laboratoire de Chémoinformatique UMR 7140 CNRS, Institut Le Bel, University of Strasbourg, 4 Rue Blaise Pascal, 67081 Strasbourg, FranceLaboratoire de Chémoinformatique UMR 7140 CNRS, Institut Le Bel, University of Strasbourg, 4 Rue Blaise Pascal, 67081 Strasbourg, FranceInstitut de Pharmacologie et de Biologie Structurale, Université de Toulouse CNRS, UPS, 205 Route de Narbonne, 31077 Toulouse, FranceInstitut de Pharmacologie et de Biologie Structurale, Université de Toulouse CNRS, UPS, 205 Route de Narbonne, 31077 Toulouse, FranceBiotechnologie et Signalisation Cellulaire UMR 7242 CNRS, École Supérieure de Biotechnologie de Strasbourg, University of Strasbourg, 300 Boulevard Sébastien Brant, 67412 Illkirch, FranceLaboratoire de Chémoinformatique UMR 7140 CNRS, Institut Le Bel, University of Strasbourg, 4 Rue Blaise Pascal, 67081 Strasbourg, FranceIn this paper, we report comprehensive experimental and chemoinformatics analyses of the solubility of small organic molecules (“fragments”) in dimethyl sulfoxide (DMSO) in the context of their ability to be tested in screening experiments. Here, DMSO solubility of 939 fragments has been measured experimentally using an NMR technique. A Support Vector Classification model was built on the obtained data using the ISIDA fragment descriptors. The analysis revealed 34 outliers: experimental issues were retrospectively identified for 28 of them. The updated model performs well in 5-fold cross-validation (balanced accuracy = 0.78). The datasets are available on the Zenodo platform (DOI:10.5281/zenodo.4767511) and the model is available on the website of the Laboratory of Chemoinformatics.https://www.mdpi.com/1420-3049/26/13/3950DMSO solubilityQSPRfragment-based screeningoutlier detectionNMR
collection DOAJ
language English
format Article
sources DOAJ
author Shamkhal Baybekov
Gilles Marcou
Pascal Ramos
Olivier Saurel
Jean-Luc Galzi
Alexandre Varnek
spellingShingle Shamkhal Baybekov
Gilles Marcou
Pascal Ramos
Olivier Saurel
Jean-Luc Galzi
Alexandre Varnek
DMSO Solubility Assessment for Fragment-Based Screening
Molecules
DMSO solubility
QSPR
fragment-based screening
outlier detection
NMR
author_facet Shamkhal Baybekov
Gilles Marcou
Pascal Ramos
Olivier Saurel
Jean-Luc Galzi
Alexandre Varnek
author_sort Shamkhal Baybekov
title DMSO Solubility Assessment for Fragment-Based Screening
title_short DMSO Solubility Assessment for Fragment-Based Screening
title_full DMSO Solubility Assessment for Fragment-Based Screening
title_fullStr DMSO Solubility Assessment for Fragment-Based Screening
title_full_unstemmed DMSO Solubility Assessment for Fragment-Based Screening
title_sort dmso solubility assessment for fragment-based screening
publisher MDPI AG
series Molecules
issn 1420-3049
publishDate 2021-06-01
description In this paper, we report comprehensive experimental and chemoinformatics analyses of the solubility of small organic molecules (“fragments”) in dimethyl sulfoxide (DMSO) in the context of their ability to be tested in screening experiments. Here, DMSO solubility of 939 fragments has been measured experimentally using an NMR technique. A Support Vector Classification model was built on the obtained data using the ISIDA fragment descriptors. The analysis revealed 34 outliers: experimental issues were retrospectively identified for 28 of them. The updated model performs well in 5-fold cross-validation (balanced accuracy = 0.78). The datasets are available on the Zenodo platform (DOI:10.5281/zenodo.4767511) and the model is available on the website of the Laboratory of Chemoinformatics.
topic DMSO solubility
QSPR
fragment-based screening
outlier detection
NMR
url https://www.mdpi.com/1420-3049/26/13/3950
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