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 ([Formula: see text] = 0.87). However, PM7-based model has comparable values of quality parameters ([Formula: see text] = 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.
journal_name
J Comput Aided Mol Desjournal_title
Journal of computer-aided molecular designauthors
Rybinska A,Sosnowska A,Barycki M,Puzyn Tdoi
10.1007/s10822-016-9894-3subject
Has Abstractpub_date
2016-02-01 00:00:00pages
165-76issue
2eissn
0920-654Xissn
1573-4951pii
10.1007/s10822-016-9894-3journal_volume
30pub_type
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