A quasi-SMILES based QSPR Approach towards the prediction of adsorption energy of Ziegler − Natta catalysts for propylene polymerization

Abstract

A heterogeneous Ziegler-Natta (ZN) catalyst has been the first choice in the olefin polymerization industries. The electron donors play a major role in the ZN catalyzed polypropylene polymerization. Quasi-SMILES based QSPR models are explored for the prediction of adsorption energies and catalytic activities of three types of internal electron donors, phthalates, 1, 3-diethers and malonates compounds. The adsorption energy and polypropylene activity has been modelled with three random splits with the representations of the molecular structure by quasi-simplified molecular input line entry system. The QSPR model building is done by the Monte Carlo optimization using three methods classic scheme (CS), balance of correlations (BC) and balance of correlation with ideal slopes (BCIS). The best model for the adsorption energy could be obtained from the random split-2 by BC method, for the training set (r 2 = 0.999, q 2 =0.998),calibration set(r 2 = 0.998, q 2 =0.998), validation set (r 2 = 0.926, q 2 = 0.834) and for the invisible validation set (r 2 = 0.876, q 2 = 0.863).The robustness of these quasi-SMILES based QSPR models are further checked by external validation parameters as r m 2 , r * m 2 and randomization technique.The quasi-SMILES based QSPR model successfully applied for designing new internal donors for the ZN catalyzed propylene polymerization.

Toxicity of Carbon Nanotubes: Molecular Mechanisms, Signaling Cascades, and Remedies in Biomedical Applications

“…Moreover, they explained that the descriptors were closely associated with the toxicity indirectly. A new approach was developed by Toropov and Toropova using eclectic information as descriptors in order to predict the toxicity of nanoscale and organic materials by applying traditional descriptors, such as molecular information and physicochemical properties. − In this method, the nanoparticles’ exposure conditions and physicochemical properties are represented as “quasi-SMILES”, which are derived from the traditional SMILES . A set of data comprised of nanoparticles’ exposure conditions and physicochemical properties can be a series of character-based representations.…”

Prediction of the self‐accelerating decomposition temperature of organic peroxides

“…In recent years, the QSPR method has demonstrated and proved to be an effective tool for predicting various physicochemical properties of desired chemicals. [18][19][20][21][22][23] QSPR method relates the properties of interest to the molecular structures of chemicals, which can help to reveal the relationships between molecular structures and desired properties at the molecular level. The advantage of this approach over other methods lies in the fact that it requires only the knowledge of chemical structures instead of any other experimental properties, therefore, can be used to make a quick and efficient prediction of the desired properties.…”

“…35 In the present investigation, we would like to explore the simplified molecular input-line entry system (SMILES) based QSPR models for SADT with help of CORAL software, which is novel and unique and proved to be efficient in the prediction of different endpoints. [18][19][20][21][22][23] Moreover, the inclusion of "Index of Ideality of Correlation (IIC)" in CORAL software in building a suitable QSPR/QSAR model is encouraging. 18,[35][36][37][38][39][40] The advantages of CORAL software with IIC, motivates us to explore the possibilities in building good QSPR models for the SADT of 41 diverse organic peroxides along with varieties of statistical parameters to prove its robustness.…”

“…Another promising method for predicting thermal hazards is the quantitative structure‐property relationship (QSPR). In recent years, the QSPR method has demonstrated and proved to be an effective tool for predicting various physicochemical properties of desired chemicals 18‐23 . QSPR method relates the properties of interest to the molecular structures of chemicals, which can help to reveal the relationships between molecular structures and desired properties at the molecular level.…”

“…In the present investigation, we would like to explore the simplified molecular input‐line entry system (SMILES) based QSPR models for SADT with help of CORAL software, which is novel and unique and proved to be efficient in the prediction of different endpoints 18‐23 . Moreover, the inclusion of “Index of Ideality of Correlation ( IIC )” in CORAL software in building a suitable QSPR/QSAR model is encouraging 18,35‐40 .…”