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: Background: Hepatitis B virus (HBV) infection is an important health problem worldwide with critical outcomes. The nucleoside analog lamivudine (LMV) is a potent inhibitor of HBV polymerase and impedes HBV replication in patients with chronic hepatitis B. Treatment with LMV for long periods causes the appearance and reproduction of drug-resistant strains, rising to more than 40% after 2 years and to over 50% and 70% after 3 and 4 years, respectively. Objectives: Artificial neural networks (ANNs) were used to make predictions with regard to resistance phenotypes using biochemical and biophysical features of the YMDD sequence.Patients and Methods: The study population comprised patients who were intended for surgery in various hospitals in Tehran-Iran. An ACRS-PCR method was performed to distinguish mutations in the YMDD motif of HBV polymerase. In the training and testing stages, these parameters were used to identify the most promising optimal network. The ideal values of RMSE and MAE are zero, and a value near zero indicates better performance. The selection was performed using statistical accuracy measures, such as root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). The main purpose of this paper was to develop a new method based on ANNs to simulate HBV drug resistance using the physiochemical properties of the YMDD motif and compare its results with multiple regression models.Results: The results of the MLP in the training stage were 0.8834, 0.07, and 0.09 and 0.8465, 0.160.04 in the testing stage; for the total data, the values were 0.8549, 0.115, and 0.065 …