Abstract
The coronavirus disease (COVID-19) has led to an rush to repurpose existing drugs, although the underlying evidence base is of variable quality. Drug repurposing is a technique by taking advantage of existing known drugs or drug combinations to be explored in an unexpected medical scenario. Drug repurposing, hence, plays a vital role in accelerating the pre-clinical process of designing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repurposing depends on massive observed data from existing drugs and diseases, the tremendous growth of publicly available large-scale machine learning methods supplies the state-of-the-art application of data science to signaling disease, medicine, therapeutics, and identifying targets with the least error. In this article, we introduce guidelines on strategies and options of utilizing machine learning approaches for accelerating drug repurposing. We discuss how to employ machine learning methods in studying precision medicine, and as an instance, how machine learning approaches can accelerate COVID-19 drug repurposing by developing Chinese traditional medicine therapy. This article provides a strong reasonableness for employing machine learning methods for drug repurposing, including during fighting for COVID-19 pandemic.
Keywords: COVID-19; Deep learning; Drug repurposing; Machine learning.
【저자키워드】 COVID-19, Drug repurposing, deep learning, machine learning, 【초록키워드】 coronavirus disease, Drug repurposing, coronavirus, therapy, Diseases, Drug discovery, Therapeutics, COVID-19 pandemic, machine learning, drugs, drug, drug combinations, Data science, target, targets, disease, Signaling, Evidence, Precision, technique, growth, drug combination, machine, machine learning approaches, de novo, deep, provide, accelerate, Chinese traditional medicine, machine learning approach, 【제목키워드】 repurposing, drug, application,