Aim and Objective:
Quantitative Structure- Property Relationship (QSPR) has been
widely developed to derive a correlation between chemical structures of molecules to their known
properties. In this study, QSPR models have been developed for modeling and predicting
thermodynamic properties of 76 camptothecin derivatives using molecular descriptors.
Materials and Methods:
Thermodynamic properties of camptothecin such as the thermal energy,
entropy and heat capacity were calculated at Hartree–Fock level of theory and 3-21G basis sets by
Gaussian 09.
Results:
The appropriate descriptors for the studied properties are computed and optimized by the
genetic algorithms (GA) and multiple linear regressions (MLR) method among the descriptors
derived from the Dragon software. Leave-One-Out Cross-Validation (LOOCV) is used to evaluate
predictive models by partitioning the total sample into training and test sets.
Conclusion:
The predictive ability of the models was found to be satisfactory and could be used for
predicting thermodynamic properties of camptothecin derivatives.
Integrated machine learning-quantitative structure property relationship (ML-QSPR) and chemical kinetics for high throughput fuel screening toward internal combustion engine
