Optimal Descriptor Based QSPR Models for Catalytic Activity of Propylene Polymerization

A heterogeneous Ziegler–Natta (ZN) catalyst is an important catalyst in the field of the polypropylene polymerization industry. The role of electron donors has been crucial in the ZN catalyzed polypropylene polymerization process. In this article, quasi-SMILES-based QSPR models are elaborated for the prediction of catalytic activities. The representations of the molecular structure by quasi-simplified molecular input line entry system were the basis to build the desired QSPR model. These models were developed by means of the Monte Carlo optimization involving the available methods classic scheme (CS), balance of correlations (BC) and balance of correlation with ideal slopes (BCIS). The best QSPR model showed r2 = 0.813 (for external validation set), rm2 (avg)=0.73 and ∆rm2= 0.03.

QSPR/QSAR Analyses by Means of the CORAL Software

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.

QSPR/QSAR Analyses by Means of the CORAL Software

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.