Abstract
AbstractA continuous and undefined malignant growth in cancer makes it an extremely heterogeneous complex disease. Different types of enzymes helps in detection of cancerous growth in the human body. In this work, different predictive Quantitative Structure‐Activity Relationship (QSAR) models by means of various molecular modeling techniques using 43 novel 6, 7‐disubstituted‐4‐phenoxyquinoline derivatives acting as Tyrosine‐protein kinase Met or hepatocyte growth factor receptor (HGFR) (c‐Met kinase) inhibitors were designed. Best QSAR models were generated through Auto QSAR. Predicted activity of these models was compared with the observed activity from literature, and it was observed that potent compound 18 b gave high docking results. Auto‐QSAR technique provided the perfect model for the designed derivatives. Binding affinity of compounds for c‐Met kinase enzyme was studied by molecular docking and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA dG) binding studies. Optimized compounds were subjected to in silico ADMET studies for predicting drug‐likeliness and toxicity properties. Reported work will assist to design, refine and construct the novel phenoxyquinoline derivatives as potent c‐Met kinase inhibitors in near future.
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