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Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids

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Abstract

Computational techniques, such as Quantitative Structure-Property Relationship (QSPR) modeling, are very useful in predicting physicochemical properties of various chemicals. Building QSPR models requires calculating molecular descriptors and the proper choice of the geometry optimization method, which will be dedicated to specific structure of tested compounds. Herein, we examine the influence of the ionic liquids’ (ILs) geometry optimization methods on the predictive ability of QSPR models by comparing three models. The models were developed based on the same experimental data on density collected for 66 ionic liquids, but with employing molecular descriptors calculated from molecular geometries optimized at three different levels of the theory, namely: (1) semi-empirical (PM7), (2) ab initio (HF/6-311+G*) and (3) density functional theory (B3LYP/6-311+G*). The model in which the descriptors were calculated by using ab initio HF/6-311+G* method indicated the best predictivity capabilities (\({\text{Q}}_{\text{EXT}}^{2}\) = 0.87). However, PM7-based model has comparable values of quality parameters (\({\text{Q}}_{\text{EXT}}^{2}\) = 0.84). Obtained results indicate that semi-empirical methods (faster and less expensive regarding CPU time) can be successfully employed to geometry optimization in QSPR studies for ionic liquids.

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Acknowledgments

Authors would like to express gratitude to Prof Paola Gramatica for access to QSARINS software. This material is based on research funded by the National Science Center (Poland) (Grant No. UMO-2012/05/E/NZ7/01148).

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  1. Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdańsk, Poland

    Anna Rybinska, Anita Sosnowska, Maciej Barycki & Tomasz Puzyn

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Rybinska, A., Sosnowska, A., Barycki, M. et al. Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids. J Comput Aided Mol Des 30, 165–176 (2016). https://doi.org/10.1007/s10822-016-9894-3

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