Ceronapril

The permeability of the mols. through the cultured Caco-2 cells is an established in vitro method for the assessment of the absorption of oral drugs.The computational approach for predicting the cellular permeability of mols. may potentiate the screening of new drugs.In this study, gradient boosted tree (GBT) approach based qual. and quant. structure-activity relationship (SAR) models have been established for binary classification (moderate-poor and highly permeable) and permeability prediction of mols. using the Caco-2 cell dataset.The structural diversity of the chems. and nonlinear structure in the considered data were tested by the similarity index and Brock-Dechert-Scheinkman statistics.The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in QSAR literature.In complete data, the qual. SAR model rendered classification accuracy of 99.26%, while the quant. SAR model yielded a correlation (R²) of 0.917 between the measured and predicted permeability values with the mean squared error (MSE) of 0.08.The results suggest for the appropriateness of the developed SAR models to reliably predict the cellular permeability of diverse chems. in Caco-2 cells and can be useful tools for initial screening of mols. in the drug development process.