2D-QSAR modeling, drug-likeness studies, ADMET prediction, and molecular docking for anti-lung cancer activity of 3-substituted-5-(phenylamino) indolone derivatives

In this work, 2D-quantitative structure-activity relationship (QSAR) studies were performed on a set of 40 indolone derivative hybrids; the 40 indolone derivatives were optimized using the DFT/6-31G/B3LYP basis to calculate the electronic descriptors, and for calculation of geometric, lipophilic, and physicochemical descriptors, we used the MM2 method. After the calculation of descriptors, the mathematical method of multiple linear regressions was explored to build the reliable QSAR model (R-Train(2) = 0.70, R-Test(2) = 0.72, and R-CV(2) = 0.70), and then the obtained QSAR model was tested with the artificial neural network method which shows high performance in model predictability; the predicted model MLR was successfully validated by external and internal validation. A molecular docking analysis was performed for two compounds (the most active molecule is molecule number 4 and molecule number 13 the least active) in dataset against protein kinase I subunit (PDK1) identified by PDB ID: 2PE1, and the docking results obtained showed that the most important hydrogen interactions are LYS11 and GLU166; to ensure the validation of the molecular docking procedure, we perform a re-docking. Finally, the new indolone compounds were evaluated for their ADMET properties and drug similarity. These results would be of great value in optimizing the discovery of new candidate anticancer drugs.

Automated summary

In this study, 2D-quantitative structure-activity relationship (QSAR) analysis was performed on a set of indolone derivative hybrids. A reliable QSAR model was built using multiple linear regression and validated using artificial neural network method. Molecular docking analysis and ADMET evaluation provided valuable insights for optimizing the discovery of new candidate anticancer drugs.