Comparative QSAR Modeling for Predicting Anticancer Potency of
Imidazo[4,5-b]Pyridine Derivatives Using GA-MLR and BP-ANN Techniques

Mahdi      Jafari; Tahereh   Momeni   Isfahani; Fatemeh      Shafiei; Masumeh   Abdoli   Senejani; Mohammad      Alimoradi

doi:10.2174/1570180820666221207121031

Abstract

Background: Prediction of toxicity of imidazo[4,5-b]pyridine derivatives is carried out using GA-MLR and BPANN methods.

Objective: A quantitative structure-property relationship (QSPR) was determined based on methods, including genetic algorithm-multiple linear regression (GA-MLR) and backpropagation artificial neural network (BP-ANN). These methods were employed for modeling and predicting the anticancer potency of imidazo[4,5-b]pyridine derivatives.

Materials and Methods: A dataset of imidazo[4,5-b]pyridine derivatives was randomly divided into two groups, training and test sets consisting of 75% and 25% of data points, respectively. The optimized conformation of compounds was obtained using the DFT-B3LYP method and 6-31G* basis sets level with Gaussian 09 software. A large number of molecular descriptors were calculated using Dragon software. The QSAR models were optimized using multiple linear regressions (MLR).

Results: The most relevant molecular descriptors were obtained using the genetic algorithm (GA) and backward stepwise regression. The predictive powers of the GA-MLR models were studied using leaveone- out (LOO) cross-validation and an external test set.

Conclusion: The obtained results of statistical parameters showed the BP-ANN model to have better performance compared to the GA-MLR model.

To assess the predictive ability of QSAR models, many statistical terms, such as correlation coefficient (R²), leave-one-out cross-validation (LOOCV), root mean squared error (RMSE), and external and internal validation were used. The results of validation methods demonstrate the QSAR model to be robust and with high predictivity.

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Comparative QSAR Modeling for Predicting Anticancer Potency of Imidazo[4,5-b]Pyridine Derivatives Using GA-MLR and BP-ANN Techniques

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