  Drug target interaction (DTI) prediction is a foundational task for in silico
drug discovery, which is costly and time-consuming due to the need of
experimental search over large drug compound space. Recent years have witnessed
promising progress for deep learning in DTI predictions. However, the following
challenges are still open: (1) the sole data-driven molecular representation
learning approaches ignore the sub-structural nature of DTI, thus produce
results that are less accurate and difficult to explain; (2) existing methods
focus on limited labeled data while ignoring the value of massive unlabelled
molecular data. We propose a Molecular Interaction Transformer (MolTrans) to
address these limitations via: (1) knowledge inspired sub-structural pattern
mining algorithm and interaction modeling module for more accurate and
interpretable DTI prediction; (2) an augmented transformer encoder to better
extract and capture the semantic relations among substructures extracted from
massive unlabeled biomedical data. We evaluate MolTrans on real world data and
show it improved DTI prediction performance compared to state-of-the-art
baselines.
