QSAR Study, Molecular Docking and Molecular Dynamic Assisted Design of Novel Quinazoline Derivatives as Anticancer Agents for Breast Cancer

One of the deadly diseases affecting the global public is cancer, and more than 85% of cases are breast cancer. In the current investigation, QSAR studies have been performed on 31 analogs of the quinazoline derivatives, which have anti-proliferative activity against the cancer cell line MCF-7. The machine learning (ML)-based SVM technique exhibited a good correlation between fitted and observed biological activities. Internal and external validations have assessed and validated the descriptive and predictive performances of QSAR models. This model demonstrates an R2 value of 0.749 and an R2 test value of 0.991. Here, seven new molecular structures are designed using the QSAR model predictions, and their potential interaction mode with the VEGFR-2 receptor (PDB ID: 3VHE) is analyzed using molecular docking and pharmacokinetic parameters. Based on QSAR model predictions, molecular docking, dynamic, and ADMET in silico property assessments, we recommend one newly designed molecule as an anti-breast cancer drug remain further in vivo and in vitro investigations before clinical trials.