QSAR, molecular docking and ADMET properties in silico studies of novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2-Yl derivatives derived from dimedone as potent anti-tumor agents through inhibition of C-Met receptor tyrosine kinase

A quantitative structure-activity relationship (QSAR) study is performed on 48 novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2 derivatives as anticancer agents capable of inhibiting c-Met receptor tyrosine kinase. The present study is conducted using multiple linear regression, multiple nonlinear regression and artificial neural networks. Three QSAR models are developed after partitioning the database into two sets (training and test) via the k-means method. The obtained values of the correlation coefficients by the three developed QSAR models are 0.90, 0.91 and 0.92, respectively. The resulting models are validated by using the external validation, leave-one-out cross-validation, Y-randomization test, and applicability domain methods. Moreover, we evaluated the drug-likeness properties of seven selected molecules based on their observed high activity to inhibit the c-Met receptor. The results of the evaluation showed that three of the seven compounds present drug-like characteristics. In order to identify the important active sites for the inhibition of the c-Met receptor responsible for the development of cancer cell lines, the crystallized form of the Crizotinib-c-Met complex (PDB code: 2WGJ) is used. These sites are used as references in the molecular docking test of the three selected molecules to identify the most suitable molecule for use as a new c-Met inhibitor. A comparative study is conducted based on the evaluation of the predicted properties of ADMET in silico between the candidate molecule and the Crizotinib inhibitor. The comparison results show that the selected molecule can be used as new anticancer drug candidates.

Automated summary

This study conducted a QSAR analysis on 48 novel compounds as anticancer agents, developing three reliable models with high correlation coefficients. Through various validation methods, drug-like properties were identified in three molecules, leading to the identification of a new potential anticancer drug candidate.