2019/08/15 by Bonggun Shin, Sung Soo Park, Shin, Bonggun +5 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Chemical Synthesis and Analysis #Computational Drug Discovery Methods #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science
paper · pdf · doi:10.48550/arxiv.1908.06760
openalex publication_date 2019/08/15 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
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.