Issue 9, 2024

Solvmate – a hybrid physical/ML approach to solvent recommendation leveraging a rank-based problem framework

Abstract

The solubility in a given organic solvent is a key parameter in the synthesis, analysis and chemical processing of an active pharmaceutical ingredient. In this work, we introduce a new tool for organic solvent recommendation that ranks possible solvent choices requiring only the SMILES representation of the solvents and solute involved. We report on three additional innovations: first, a differential/relative approach to solubility prediction is employed, in which solubility is modeled using pairs of measurements with the same solute but different solvents. We show that a relative framing of solubility as ranking solvents improves over a corresponding absolute solubility model across a diverse set of selected features. Second, a novel semiempirical featurization based on extended tight-binding (xtb) is applied to both the solvent and the solute, thereby providing physically meaningful representations of the problem at hand. Third, we provide an open-source implementation of this practical and convenient tool for organic solvent recommendation. Taken together, this work could be of benefit to those working in diverse areas, such as chemical engineering, material science, or synthesis planning.

Graphical abstract: Solvmate – a hybrid physical/ML approach to solvent recommendation leveraging a rank-based problem framework

Supplementary files

Article information

Article type
Paper
Submitted
22 May 2024
Accepted
19 Jul 2024
First published
30 Jul 2024
This article is Open Access
Creative Commons BY-NC license

Digital Discovery, 2024,3, 1749-1760

Solvmate – a hybrid physical/ML approach to solvent recommendation leveraging a rank-based problem framework

J. Wollschläger and F. Montanari, Digital Discovery, 2024, 3, 1749 DOI: 10.1039/D4DD00138A

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements