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2000
Volume 17, Issue 6
  • ISSN: 1573-4099
  • E-ISSN: 1875-6697

Abstract

Introduction: Quantitative structure-property relationships (QSPRs) models have been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been used on 91 alkenes to develop a robust model for the prediction of enthalpy of vaporization under standard condition (ΔH°/kJ.mol-1) and at normal temperature of boiling points (T˚bp /K) of alkenes. Methods: A training set of 81 structurally diverse alkenes was randomly selected and used to construct QSPR models. These models were optimized using backward-multiple linear regression (MLR) analysis. The genetic algorithm and multiple linear regression analysis (GA-MLR) were used to select the suitable descriptors derived from the Dragon software. Results: The multicollinearity properties of the descriptors contributed in the QSPR models were tested and several methods were used for testing the predictive models power such as Leave-One- Out (LOO) cross-validation(Q2 LOO), the five-fold cross-validation techniques, external validation parameters (Q2, Q2, Q2), the concordance correlation coefficient (CCC) and the predictive parameter R2 m. Conclusion: The predictive ability of the models was found to be satisfactory, and the five descriptors in three blocks, namely connectivity, edge adjacency indices and 2D matrix-based descriptors could be used to predict the mentioned properties of alkenes.

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/content/journals/cad/10.2174/1573409916666200625141758
2021-10-01
2025-06-15
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