The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions(DTIs) has become a crucial step in early stages of drug discovery. Consequently, developing computational approaches capable of identifying potential DTIs with minimum error rate are increasingly being pursued. If the strength of the binding between a drug and its target is not sufficiently strong, such DTI may not be useful. Therefore, the methods developed to predict drug-target binding affinities(DTBA) are of great value. In this seminar, we will review the basic concepts of predicting DTBA. We focus on the methods developed using machine learning, and specially deep learning approaches, as well as related benchmark datasets and databases.