Computer-aided anti-cancer drug discovery of EGFR protein based on virtual screening of drug bank, ADMET, docking, DFT and molecular dynamic simulation studies

AbstractNumerous malignancies, including breast cancer, non-small cell lung cancer, and chronic myeloid leukemia, are brought on by aberrant tyrosine kinase signaling. Since the current chemotherapeutic medicines are toxic, there is a great need and demand from cancer patients to find novel chemicals that are toxic-free or have low toxicity and that can kill tumor cells and stop their growth. This work describes the in-silico examination of substances from the drug bank as EGFR inhibitors. Firstly, drug-bank was screened using the pharmacophore technique to select the ligands and Erlotinib (DB00530) was used as matrix compound. The selected ligands were screened using ADMET and the hit compounds were subjected to docking. The lead compound from the docking was subjected to DFT and MD simulation study. Using the pharmacophore technique, 23 compounds were found through virtual drug bank screening. One hit molecule from the ADMET prediction was the subject of docking study. According to the findings, DB03365 molecule fits to the EGFR active site by several hydrogen bonding interactions with amino acids. Furthermore, DFT analysis revealed high reactivity for DB03365 compound in the binding pocket of the target protein, based on ELUMO, EHOMO and band energy gap. Furthermore, MD simulations for 100 ns revealed that the ligand interactions with the residues of EGFR protein were part of the essential residues for structural stability and functionality. However, DB03365 was a promising lead molecule that outperformed the reference compound in terms of performance and in-vitro and in-vivo experiments needs to validate the study.Communicated by Ramaswamy H. SarmaKeywords: Anti-cancervirtual screeningdockingADMETDFTmolecular dynamic simulation AcknowledgmentsThe authors would like to thank Syed Awais Attique, Department of Biological Sciences, National University of Singapore, Bioinformatics Institute, Agency of Science, Technology and Research, Singapore..Disclosure statementThe authors have no conflict of interest.Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.Additional informationFundingThe authors would like to thanks to the Malaysia Cocoa Board for the grant to the Universiti Malaysia Pahang (University Reference Number: RDU210710) for this project.