  Predicting drug-target interactions (DTI) is an essential part of the drug
discovery process, which is an expensive process in terms of time and cost.
Therefore, reducing DTI cost could lead to reduced healthcare costs for a
patient. In addition, a precisely learned molecule representation in a DTI
model could contribute to developing personalized medicine, which will help
many patient cohorts. In this paper, we propose a new molecule representation
based on the self-attention mechanism, and a new DTI model using our molecule
representation. The experiments show that our DTI model outperforms the state
of the art by up to 4.9% points in terms of area under the precision-recall
curve. Moreover, a study using the DrugBank database proves that our model
effectively lists all known drugs targeting a specific cancer biomarker in the
top-30 candidate list.
