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Gilles Marcou

Assistant Professor

University of Strasbourg
Country flagFrance

Research Interests

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About

Gilles Marcou is an Assistant Professor at the University of Strasbourg, France. His research focuses on various areas including absorption modeling, dimensionality reduction for chemical visualization, and medicinal chemistry. He has authored recent articles that explore predictive modeling in antimicrobial resistance and methodologies for chemical data visualization.

Articles (27)

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2026

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2026

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2026

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2026

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2026

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2025

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2025

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2025

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2025

Kinetic aqueous or buffer solubility is important parameter measuring suitability of compounds for high throughput assays in early drug discovery while thermodynamic solubility is reserved for later stages of drug discovery and development. Kinetic solubility is also considered to have low inter‐laboratory reproducibility because of its sensitivity to protocol parameters [1]. Presumably, this is why little efforts have been put to build QSPR models for kinetic in comparison to thermodynamic aqueous solubility. Here, we investigate the reproducibility and modelability of kinetic solubility assays. We first analyzed the relationship between kinetic and thermodynamic solubility data, and then examined the consistency of data from different kinetic assays. In this contribution, we report differences between kinetic and thermodynamic solubility data that are consistent with those reported by others [1, 2] and good agreement between data from different kinetic solubility campaigns in contrast to general expectations. The latter is confirmed by achieving high performing QSPR models trained on merged kinetic solubility datasets. The poor performance of QSPR model trained on thermodynamic solubility when applied to kinetic solubility dataset reinforces the conclusion that kinetic and thermodynamic solubilities do not correlate: one cannot be used as an ersatz for the other. This encourages for building predictive models for kinetic solubility. The kinetic solubility QSPR model developed in this study is freely accessible through the Predictor web service of the Laboratory of Chemoinformatics ( https://chematlas.chimie.unistra.fr/cgi‐bin/predictor2.cgi ).

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2022

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2022

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2022

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2022

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2022

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2022

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2022

Collaborators (7)

Stefano Pieraccini

Associate Professor

Università degli Studi di Milano Dipartimento di Matematica Federigo Enriques

ITALY
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Patrick Fischer

Assistant Professor

Medical University of Graz

AUSTRIA
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Alexandre Varnek

Professor

-

FRANCE
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Artem Cherkasov

Professor

The University of British Columbia

CANADA
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Alexey Orlov

Maître de conférences (Assistant Professor)

University of Strasbourg

FRANCE
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Benoit Creton

IFP Energies nouvelles

FRANCE
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Olexandr Ye. Pashenko

-

UKRAINE
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