QSPR study to predict some of quantum chemical properties of anticancer imidazo[4,5‐b]pyridine derivatives using genetic algorithm multiple linear regression and molecular descriptors

Pyridine and its derivatives have been applied clinically for the treatment of a wide range of diseases and in the synthesis of novel drugs. In the present work, imidazo[4,5‐b]pyridine derivatives as anticancer drugs were exhibited to select the important descriptor for quantum chemical properties. Two of the fundamental thermodynamic properties are heat capacity (Cv) and entropy (S), which are important in the field of chemical kinetics and are key in the understanding and design of chemical processes involving chemical reactions. A Quantitative Structure–Property Relationship (QSPR) study was used to predict the quantum chemical properties like Cv and S of 105 imidazole derivatives using molecular descriptors and the genetic algorithm–multiple linear regression (GA–MLR). The best QSPR models were selected using criteria coefficients such as R2, R2adj, RMSE and Fisher ratio. Different internal and external validation metrics were adopted to evaluate the stability, fit and predictive power of the QSPR models. The validation results and statistical analysis show that the models possess good prediction power and robustness, and the total size (TS) and Sanderson electronegativity(RDF060e) and total information content index(TIC1) of imidazo[4,5‐b]pyridine derivatives are increasingly related to the studied properties. A comprehensive analysis followed, including residual plots that compared the observed entropy (S) values with their corresponding values for the 105 imidazo[4,5‐b]pyridine derivatives across both the training and test datasets. This analysis was conducted using the GA‐MLR method. A parallel investigation was conducted by plotting residuals against the observed heat capacity (Cv) values. The equations for Cv and S, along with the selected descriptors, are presented herein.