In Silico Prediction of EGFR Inhibitors from Thiophene Derivatives

Abstract

Cancer is one of the biggest global health problems and is the second leading cause of death worldwide. Cancer also causes great damage to economy. Unfortunately, there is still no effective treatment method against this disease today, and the mortality rates in certain types are still very high. Medical research can now be done faster and safer with the aid of in silico studies. These studies save time for researchers and accelerate new drug discoveries. In our study, thiophene derivatives with important efficacy in cancer treatment were focused on and the affinity of the small molecule structures determined as candidates to the Epidermal Growth Factor Receptor (EGFR), known to be the key receptor in cancer, was examined. First, molecular docking studies were performed, and then long-term molecular dynamics (MD) simulations were carried out. Finally, anti-cancer activity predictions based on Quantitative Structure-Activity Relationship (QSAR) were performed. Co-crystallized ligand Erlotinib, taken from the Protein Data Bank (PDB), was used as a positive control and compared with candidate drugs using the same procedures. In light of the analysis of virtual screening, MD, MM/GBSA, and QSAR predictions, the top three molecules and their MM/GBSA scores were identified as follows: OSI 930 (-65.81 kcal/mol), Neltenexine (-49.53 kcal/mol), and Tenonitrozole (-41.95 kcal/mol). As a result, in this study, candidate molecules that inhibit EGFR and have the highest potential as anti-cancer drugs among thiophene-derived compounds were determined and detailed in silico analyzes were performed. This study holds importance as it may guide future anti-cancer drug discovery studies.

Keywords

• EGFR
• MD simulation
• Molecular docking
• Small molecules
• Thiophene derivatives

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