  Predicting drug-target interaction (DTI) is critical in the drug discovery
process. Despite remarkable advances in recent DTI models through the
integration of representations from diverse drug and target encoders, such
models often struggle to capture the fine-grained interactions between drugs
and protein, i.e. the binding of specific drug atoms (or substructures) and key
amino acids of proteins, which is crucial for understanding the binding
mechanisms and optimising drug design. To address this issue, this paper
introduces a novel model, called FusionDTI, which uses a token-level Fusion
module to effectively learn fine-grained information for Drug-Target
Interaction. In particular, our FusionDTI model uses the SELFIES representation
of drugs to mitigate sequence fragment invalidation and incorporates the
structure-aware (SA) vocabulary of target proteins to address the limitation of
amino acid sequences in structural information, additionally leveraging
pre-trained language models extensively trained on large-scale biomedical
datasets as encoders to capture the complex information of drugs and targets.
Experiments on three well-known benchmark datasets show that our proposed
FusionDTI model achieves the best performance in DTI prediction compared with
seven existing state-of-the-art baselines. Furthermore, our case study
indicates that FusionDTI could highlight the potential binding sites, enhancing
the explainability of the DTI prediction.
