Auto QSAR - a Fast Approach for the Creation and Application of QSAR Models Through Automation
Posted: 7 Feb 2020
Date Written: February 6, 2020
A continuous and undefined malignant growth is seen in cancer that makes it an extremely heterogeneous complex disease. Also there are different types of enzymes which help in the detection of cancerous growth in the human body. Here in this work, we designed different predictive QSAR models by means of various molecular modeling techniques using 43 novel 6, 7-disubstituted-4-phenoxyquinoline derivatives acting as c-Met kinase inhibitors. Auto QSAR generated best QSAR models which gave predicted activity which was then compared with the observed literature activity thus providing the perfect model for all the above derivatives. Also binding affinity of the compounds was studied by performing molecular docking studies of all the compounds on c-Met kinase enzyme as well as MM-GBSA dG binding. Moreover the obtained compounds were subjected to in silico ADME studies to screen for their drug-likeness and toxicity. With the help of the above QSAR study it will be easier to design, refine and construct the novel phenoxyquinoline derivatives as potent c-Met kinase inhibitors in the near future.
Keywords: Auto-QSAR, prediction, activity, c-Met kinase, phenoxyquinoline, docking
Suggested Citation: Suggested Citation