QSAR modeling of pyrazoline derivative as carbonic anhydrase inhibitors

The efficacy of 34 pyrazoline derivatives as carbonic anhydrase inhibitors was studied in silico. The quantum descriptors were calculated by the DFT/B3LYP method using the 6-31G(d) basis; the dataset was randomly divided into training and testing. By altering the compounds in the sets, four models were created, and they were then used to determine the predicted pIC50 values for the six chemicals in the test set. According to the OECD guidelines for QSAR model validation and the Golbraikh and Tropsha's criteria for model approval, each created model was independently validated both internally and externally, along with YRandomization. Model 3 is chosen because it has higher R-2, R-2test, and Q(2cv) values (R-2 = 0.79, R-test(2) = 0.95, Q(cv)(2) = 0.64). Only one descriptor has a proportional influence on pIC50 activity, but the other four descriptors have an inverse influence on pIC50 because of the negative contribution coefficient. Given the descriptors of the model, we could propose new molecules with remarkable inhibitory activity.

Automated summary

In this study, the efficacy of 34 pyrazoline derivatives as carbonic anhydrase inhibitors was investigated using computational methods. Quantum descriptors were calculated, and four models were created to predict the pIC50 values of six chemicals in a test set. Each model was validated internally and externally, and Model 3 was chosen based on its higher R-2, R-test(2), and Q(cv)(2) values (R-2 = 0.79, R-test(2) = 0.95, Q(cv)(2) = 0.64). New molecules with significant inhibitory activity could be proposed based on the descriptors of the model.