Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction
This work addresses the high cost and time of drug discovery, potentially reducing healthcare expenses and aiding personalized medicine for patient cohorts, though it appears incremental in improving existing DTI models.
The paper tackles the problem of predicting drug-target interactions by proposing a new self-attention-based molecule representation and model, which outperforms state-of-the-art methods by up to 4.9% in area under the precision-recall curve and effectively identifies known drugs for a cancer biomarker in top-30 candidate lists.
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.