4.6 Article

Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

Journal

MOLECULES
Volume 26, Issue 19, Pages -

Publisher

MDPI
DOI: 10.3390/molecules26195779

Keywords

DES; density; cheminformatics; QSPR; validation; consensus modelling; thermophysical properties

Funding

  1. Fundacao para a Ciencia e a Tecnologia (FCT/MECS) [UID/QUI/50006/2020]
  2. European Union [725034]
  3. European Research Council (ERC) [725034] Funding Source: European Research Council (ERC)

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This study proposed QSPR models for estimating the density of various DES, which were rigorously validated and showed robustness and reliability. Structural analysis revealed key features influencing DES density, and a consensus prediction approach was used to develop a model with improved predictive accuracy. Publicly available tools were utilized for model derivation to ensure reproducibility of the proposed methodology, with future work potentially focusing on developing interpretable cheminformatic models for other thermodynamic properties of DES.
Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES-and because the vast majority of DES has yet to be synthesized-the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.

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