Binding site prediction for new proteins is important in structure-based drug design . The identified binding sites may be helpful in the development of treatments for new viral outbreaks in the world when there is no information available about their pockets with Covid-19 being a case in point . Identification of the pockets using computational methods, as an alternative method, has recently attracted much interest . In this study, the binding site prediction is viewed as a semantic segmentation problem . An improved 3D version of the U-Net model based on the dice loss function is utilized to predict the binding sites accurately . The performance of the proposed model on the independent test datasets and SARS-COV-2 shows the segmentation model could predict the binding sites with a more accurate shape than the recently published deep learning model, i.e . DeepSite . Therefore, the model may help predict the binding sites of proteins and could be used in drug design for novel proteins.