The human Epidermal Growth Factor (EGFR) plays an important role in signaling pathways, such as cell proliferation and migration. Mutations like G719S, L858R, T790M, G719S/T790M or T790M/L858R can alter its conformation, and, therefore, drug responses from lung cancer patients. In this context, candidate drugs are being tested and in silico studies are necessary to know how these mutations affect the ligand binding site. This problem can be tackled by using a multi-objective approach applied to the molecular docking problem. According to the literature, few studies are related to the application of multi-objective approaches by minimizing two or more objectives in drug discovery. In this study, we have used four algorithms (NSGA-II, GDE3, SMPSO and MOEA/D) to minimize two objectives: the ligand-receptor intermolecular energy and the RMSD score. We have prepared a set of instances that includes the wild-type EGFR kinase domain and the same receptor with somatic mutations, and then we assessed the performance of the algorithms by applying a quality indicator to evaluate the convergence and diversity of the reference fronts. The MOEA/D algorithm yields the best solutions to these docking problems. The obtained solutions were analyzed, showing promising results to predict candidate EGFR inhibitors by using this multi-objective approach.
Keywords: Epidermal Growth Factor Receptor; Epidermal Growth Factor Receptor mutants; drug resistance; epidermal growth factor; metaheuristics; molecular docking; multi-objective optimization.