Predicting aqueous and organic solubilities with machine learning: a workflow for identifying organic cosolvents

Q: What type of study is this?

This is a Quantitative Study study.

Maurycy Krzyżanowski; Sirazam Munira Aishee; Nirala Singh; Bryan R. Goldsmith

doi:10.1039/d5dd00134j

January 1, 2025Digital DiscoveryOpen Access

Predicting aqueous and organic solubilities with machine learning: a workflow for identifying organic cosolvents

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Key Points

Predictive models identify suitable organic cosolvents for various applications, enhancing solvent selection.
The study demonstrates a novel workflow utilizing machine learning to predict solubility in organic and aqueous environments.
This approach supports faster solvent selection in fields like pharmaceutical development and electrochemical applications.
Potential implications include optimizing processes in drug development and improving electrochemical conversion efficiency.

Implication

This research develops predictive models for solubility in organic systems, suggesting efficient solvent selection methods.

Abstract

Developing predictive models of solubility is useful for accelerating solvent selection for applications ranging from electrochemical conversion of organics to pharmaceutical drug development. Herein, we report on the development of...

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Cite This Study

Krzyżanowski et al. (2025) studied this question.

synapsesocial.com/papers/68d4508931b076d99fa584ac https://doi.org/10.1039/d5dd00134j

View Full Paper

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Predicting aqueous and organic solubilities with machine learning: a workflow for identifying organic cosolvents

Key Points

Implication

Abstract

Discussion

Cite This Study

Predicting aqueous and organic solubilities with machine learning: a workflow for identifying organic cosolvents

Key Points

Implication

Abstract

Discussion

Cite This Study

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