  Predicting drug-target interaction is key for drug discovery. Recent deep
learning-based methods show promising performance but two challenges remain:
(i) how to explicitly model and learn local interactions between drugs and
targets for better prediction and interpretation; (ii) how to generalize
prediction performance on novel drug-target pairs from different distribution.
In this work, we propose DrugBAN, a deep bilinear attention network (BAN)
framework with domain adaptation to explicitly learn pair-wise local
interactions between drugs and targets, and adapt on out-of-distribution data.
DrugBAN works on drug molecular graphs and target protein sequences to perform
prediction, with conditional domain adversarial learning to align learned
interaction representations across different distributions for better
generalization on novel drug-target pairs. Experiments on three benchmark
datasets under both in-domain and cross-domain settings show that DrugBAN
achieves the best overall performance against five state-of-the-art baselines.
Moreover, visualizing the learned bilinear attention map provides interpretable
insights from prediction results.
