Quasi-SMILES as a basis for the development of QSPR models to predict the CO2 capture capacity of deep eutectic solvents using correlation intensity index and consensus modelling

Abstract

The interpretation of the CO2 capture capacity of the deep eutectic solvents (DESs) as a mathematical function of (i) hydrogen bond acceptor(HBA); (ii) hydrogen bond donor (HBD); (iii) Molar ratio (HBA: HBD) (iv) temperature in kelvin and (v) p (MPa) gives quantitative models with good statistical quality. The quasi-SMILES are employed to represent the DESs (deep eutectic solvents under different conditions) for building up QSPR models. Quasi-SMILES differ from regular SMILES (simplified molecular input-line entry system) by including extra characters that indicate experimental parameters. SMILES descriptors may be used to construct quantitative structure–property/activity relationships (QSPRs/QSARs), whilst quasi-SMILES descriptors can be employed to build quantitative models of laboratory findings under diverse situations. Four random splits are prepared from the dataset of 72 DESs and each split is further divided into four sets namely active training, passive training, calibration and validation set. The percentage of the common quasi-SMILES in a set of two splits is zero. Three target functions with and without the index of ideality of correlation (IIC) and correlation intensity index (CII) are applied to build 12 QSPR models. The statistical quality of present QSPR models (mCO2) developed by the third target function is good to excellent, with determination coefficients ranging from 0.8303 to 0.9206 for the external validation set. In the last, consensus modelling using the intelligent consensus tool of DTC lab (https://dtclab.webs.com/software-tools) is also performed to predict the robustness of the developed models. The distribution of the dataset of all splits is applied to generate different consensus models and in all cases, the consensus model is found winner. The determination coefficient of all winner consensus models was in the range of 0.9751 to 0.9910 (CM0Split1R2=0.9909;CM0Split2R2=0.9883;CM2Split3R2=0.9910;andCM3Split4R2=0.9751). The statistical results of the determination coefficient and MAE (95%) of the second consensus model of split 3 (CM2Split3R2=0.9910;CM2Split3MAE95%=0.17776) was found best than the R2 and MAE (95%) of other consensus models of all splits. Therefore, it was considered as a winner consensus model from all consensus models.