QSPR modeling of selectivity at infinite dilution of ionic liquids

• Published in: Journal of cheminformatics Vol. 13; no. 1; pp. 83 - 10
• Main Authors: Klimenko, Kyrylo, Carrera, Gonçalo V. S. M.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 26.10.2021; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Activity coefficients; Aniline; Anions; Bias; Big data; Cations; Chemical reactions; Chemistry; Chemistry and Materials Science; Combinatorial analysis; Comparative analysis; Computational Biology/Bioinformatics; Computer Applications in Chemistry; Data points; Dilution; Documentation and Information in Chemistry; Dodecane; Extractants; Ionic liquids; Ions; Keras; Liquid mixtures; Predictions; Research Article; Screening; Selectivity; Separation techniques; Separation technology; Theoretical and Computational Chemistry; Big data; Keras; Separation technology; Liquid mixtures
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-021-00562-8

The intelligent choice of extractants and entrainers can improve current mixture separation techniques allowing better efficiency and sustainability of chemical processes that are both used in industry and laboratory practice. The most promising approach is a straightforward comparison of selectivity at infinite dilution between potential candidates. However, selectivity at infinite dilution values are rarely available for most compounds so a theoretical estimation is highly desired. In this study, we suggest a Quantitative Structure–Property Relationship (QSPR) approach to the modelling of the selectivity at infinite dilution of ionic liquids. Additionally, auxiliary models were developed to overcome the potential bias from big activity coefficient at infinite dilution from the solute. Data from SelinfDB database was used as training and internal validation sets in QSPR model development. External validation was done with the data from literature. The selection of the best models was done using decision functions that aim to diminish bias in prediction of the data points associated with the underrepresented ionic liquids or extreme temperatures. The best models were used for the virtual screening for potential azeotrope breakers of aniline +  n -dodecane mixture. The subject of screening was a combinatorial library of ionic liquids, created based on the previously unused combinations of cations and anions from SelinfDB and the test set extractants. Both selectivity at infinite dilution and auxiliary models show good performance in the validation. Our models’ predictions were compared to the ones of the COSMO-RS, where applicable, displaying smaller prediction error. The best ionic liquid to extract aniline from n -dodecane was suggested.