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Updated: Oct 30, 2025

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

DMSO Solubility Assessment for Fragment-Based Screening.

Shamkhal Baybekov¹, Gilles Marcou¹, Pascal Ramos²

¹Laboratoire de Chémoinformatique UMR 7140 CNRS, Institut Le Bel, University of Strasbourg, 4 Rue Blaise Pascal, 67081 Strasbourg, France.

Molecules (Basel, Switzerland)

|July 2, 2021

Summary

This summary is machine-generated.

This study measured the solubility of 939 small organic molecules in dimethyl sulfoxide (DMSO) for screening experiments. A predictive model achieved 0.78 balanced accuracy, aiding future solubility assessments.

Keywords:

DMSO solubility NMR QSPR fragment-based screening outlier detection

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Last Updated: Oct 30, 2025

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

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Area of Science:

Chemical informatics
Drug discovery
Physical chemistry

Background:

Accurate solubility data is crucial for drug discovery and screening.
Dimethyl sulfoxide (DMSO) is a common solvent in screening experiments.
Predicting solubility of small organic molecules aids experimental planning.

Purpose of the Study:

To experimentally measure and predict the solubility of small organic molecules in DMSO.
To develop a chemoinformatics model for solubility prediction.
To identify and address experimental outliers in solubility measurements.

Main Methods:

Experimental measurement of DMSO solubility for 939 fragments using Nuclear Magnetic Resonance (NMR) spectroscopy.
Development of a Support Vector Classification model utilizing ISIDA fragment descriptors.
Analysis of outliers and retrospective identification of experimental issues.

Main Results:

Experimental solubility data for 939 fragments in DMSO was obtained.
A Support Vector Classification model was built, achieving a balanced accuracy of 0.78 after refinement.
34 outliers were identified, with experimental issues traced for 28 of them.

Conclusions:

The developed chemoinformatics model accurately predicts DMSO solubility for small organic molecules.
Refined solubility data and predictive models enhance the reliability of screening experiments.
Datasets and models are publicly available to support further research.