  Drug-target interaction (DTI) prediction plays a crucial role in drug
discovery, and deep learning approaches have achieved state-of-the-art
performance in this field. We introduce an ensemble of deep learning models
(EnsembleDLM) for DTI prediction. EnsembleDLM only uses the sequence
information of chemical compounds and proteins, and it aggregates the
predictions from multiple deep neural networks. This approach not only achieves
state-of-the-art performance in Davis and KIBA datasets but also reaches
cutting-edge performance in the cross-domain applications across different
bio-activity types and different protein classes. We also demonstrate that
EnsembleDLM achieves a good performance (Pearson correlation coefficient and
concordance index > 0.8) in the new domain with approximately 50% transfer
learning data, i.e., the training set has twice as much data as the test set.
