Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures

Aug 31, 2026

Uploaded by Skyler

Solubility of organic molecules underpins organic synthesis and industrial chemistry, shaping the design of phase separation and purification units and influencing environmental migration. Solvent and temperature selection often relies on trial and error because solid solubility limits are poorly known. A fast computational framework estimates solubility of solid neutral organic molecules in water and many organic solvents across broad temperature ranges. The model merges thermodynamic equations with machine learning for solvation free energy, solvation enthalpy, Abraham parameters, and aqueous solid solubility at 298 K, supported by an open SolProp dataset of 5000+ measurements.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Importance of solubility in chemical and pharmaceutical processes

Limits of existing prediction methods and data scarcity

Proposed fast computational approach and model components

[pubs.acs.org/JACS](pubs.acs.org/JACS) Article Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures Florence H. Vermeire, Yunsie Chung, and William H. Green * Cite This: J. Am. Chem. Soc. 2022, 144, 10785−10797 Read Online ACCESS Metrics & More Article Recommendations *sı Supporting Information ABSTRACT: The solubility of organic molecules is crucial in organic synthesis and industrial chemistry; it is important in the design of many phase separation and puriﬁcation units, and it controls the migration of many species into the environment. To decide which solvents and temperatures can be used in the design of new processes, trial and error is often used, as the choice is restricted by unknown solid solubility limits. Here, we present a fast and convenient computational method for estimating the solubility of solid neutral organic molecules in water and many organic solvents for a broad range of temperatures. The model is developed by combining fundamental thermodynamic equations with machine learning models for solvation free energy, solvation enthalpy, Abraham solute parameters, and aqueous solid solubility at 298 K. We provide free open-source and online tools for the prediction of solid solubility limits and a curated data collection (SolProp) that includes more than 5000 experimental solid solubility values for validation of the model. The model predictions are accurate for aqueous systems and for a huge range of organic solvents up to 550 K or higher. Methods to further improve solid solubility predictions by providing experimental data on the solute of interest in another solvent, or on the solute’s sublimation enthalpy, are also presented. ■ INTRODUCTION The solubility of solids in organic solvents plays a huge role in various chemical systems such as organic chemistry, environmental chemistry, organo(photo)catalysis, and (petro) -chemical industry. Solid−solution equilibria are, for example, used in the design of ﬂow batteries, puriﬁcation units, extraction units, and liquid-phase chemical processes. Speciﬁc to the pharmaceutical industry, the solubility of active pharmaceutical ingredients (API) in a variety of organic solvents is an important property in the development of new drugs. Many curated databases (e.g., the AqSol database 1) are limited to one or a few solvents. For example, the aqueous solid solubility is important in many applications, including the initial screening of potential APIs, and thus there are enough data available to train deep neural networks for the prediction of this property. Some examples of recent successful datadriven predictors for the aqueous solid solubility are AquaSol developed by Lusci et al.,5 the models of Cui et al.6 and by Boobier et al.,7 and SolTranNet developed by Francoeur and Koes.8 Despite the tremendous progress made in machine learning for molecular and material science,9 for the direct prediction of solid solubility in organic solvents, the application Downloaded via TSINGHUA UNIV on November 5, 2022 at 21:07:48 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles. available for the aqueous solid solubility of APIs as this property is of interest in the initial screening process of potential drugs. Further in the drug development chain during lab-scale synthesis, puriﬁcation, crystallization, and scale-up from batch to continuous processes, information on the solubility of the APIs in organic solvents other than water is required. Many methods exist for the prediction of the solubility of solids in speciﬁc organic solvents. A recently published review by Kuentz et al.2 summarizes recent advances in solubility predictions. The main limitation of existing methods is that they require physical properties of the solute, such as the fusion known in the enthalpy and/or melting temperature (e.

FAQs

Why is predicting solid solubility limits important for new chemical processes?

Solubility determines which solvents and temperatures can be used, affecting phase separation, purification, and migration of species. Unknown solid solubility limits make trial-and-error selections inefficient.

What is the core idea of the proposed prediction method?

It estimates solubility of solid neutral organic molecules by combining fundamental thermodynamic equations with machine learning models for solvation properties and Abraham solute parameters, anchored to aqueous solid solubility at 298 K.

How is the model validated and supported for practical use?

The work provides open online tools and a curated SolProp dataset containing 5000+ experimental solid solubility values. Reported accuracy covers aqueous systems and a wide range of organic solvents up to 550 K or higher.

Aug 31, 2026

Uploaded by Skyler