In Journal of biomolecular structure & dynamics
The epidermal growth factor receptor (EGFR) has been shown to be extremely important in numerous signaling pathways, particularly those involved in cancer progression. Many therapeutic inhibitors, consisting of both small molecules and monoclonal antibodies, have been developed to target inflammatory, triple-negative and metastatic breast cancer. With the emergence of resistance in breast cancer treatment strategies, there is a need to develop novel drug targets that not only overcome resistance, but also exhibit low toxicity and high specificity. The work presented here focuses on the identification of new inhibitors against the EGFR protein using combined computational approaches. Using a comprehensive machine learning-based virtual screening approach complemented by other computational approaches, we identified six new molecules from the ZINC database. The gold docking score of these six novel molecules is 125.95, 125.38, 123.13, 119.71, 115.64 and 113.73, respectively, while the gold score of the control group is 120.74. In addition, we also analyzed the FEC value of these compounds and found that the values of compounds 1, 2, 3 and 4 (-61.82, -63.98, -67.98 and -63.32, respectively) were higher are than those of the control group (-61.05). Furthermore, these molecules showed highly stable RMSD plots and good interaction of hydrogen bonds. The identified inhibitors provided interesting insights for understanding the electronic, hydrophobic, steric and structural requirements for EGFR inhibitory activity. Distinguishing these novel molecules could lead to the development of new drugs useful in treating breast cancer.Communicated by Ramaswamy H. Sarma.
Siddiqui Arif Jamal, Jahan Sadaf, Patel Mitesh, Abdelgadir Abdelmushin, Alturaiki Wael, Bardakci Fevzi, Sachidanandan Manojkumar, Badraoui Riadh, Snoussi Mejdi, Adnan Mohd
2023-Feb-24
EGFR, Inhibitors, drug discovery, machine learning, molecular dynamics, pharmacophore
