Abstract
Optimal descriptors calculated with SMILES represented a structure of monomer units applied to build up a model of glass transition temperatures of diverse polymers. Quantitative structure-property relationships (QSPRs) were established for the above dataset. The statistical quality of the model of glass transition temperatures is quite good. The simplified molecular input-line entry system (SMILES) has been used to represent the molecular structure of corresponding monomers. The hybrid optimal descriptors calculated with the so-called correlation weights of molecular features extracted from SMILES and molecular hydrogen-suppressed graph (HSG) were used as the basis of the one-variable model. The index of ideality of correlation (IIC) is a new criterion of the predictive potential of the QSPR model. Here, the applicability of the IIC as a tool to improve the predictive potential of the model for glass transition temperatures is confirmed.
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