In the Big Data era, the amount of digital data is increasing exponentially. Knowledge graphs are gaining attention to handle the variety dimension of Big Data, allowing machines to understand the semantics present in data. For example, knowledge graphs such as STITCH, SIDER, and Drug-Bank have been developed in the Biomedical domain. As the number of data increases, it is critical to perform analysis of data. Interaction network analysis is especially important in knowledge graphs, e.g., to detect drug-target interaction. Having a good target identification approach helps in accelerating and reducing the cost of discovering new medicines. In this work, we propose a machine learning-based approach that combines two inputs: (1) interactions and similarities among entities, and (2) translation to embeddings technique. We focus on the problem of discovering missing links in the data, called link prediction. Our approach, named SimTransE, is able to analyze the drug-target interactions and similarities. Based on this analysis, SimTransE is able to predict new drug-target interactions. We empirically evaluate SimTransE using existing benchmarks and evaluation protocols defined by existing state-of-the-art approaches. Our results demonstrate the good performance of SimTransE in the task of link prediction.