期刊
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
卷 14, 期 1, 页码 15-21出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s12539-021-00487-8
关键词
Machine learning; Deep learning; COVID-19; Drug repurposing
资金
- China Postdoctoral Science Foundation [2019M662373]
- National Key Research and Development Program of China [2020YFC2003500, 2017YFB0202602, 2018YFC0910405, 2017YFC1311003, 2016YFC1302500, 2016YFB0200400, 2017YFB0202104]
This article discusses the importance of drug repurposing in accelerating novel drug design and explores the application of machine learning methods in this process. It provides insights on how machine learning approaches can be used to accelerate COVID-19 drug repurposing, using Chinese traditional medicine therapy as an example.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据