iATC_Deep-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals by Deep Learning

• 作者: Kuo-Chen Chou Zhe Lu
• 来源期刊: 生命科学与技术进展(英文)
• 学科: 医学
• 关键词: PANDEMIC CORONAVIRUS MULTI-LABEL System ANATOMICAL THERAPEUTIC CHEMICALS Learning at Deeper Level Five-Steps Rule
• 年，卷（期）: 2020,（5）
• 页码范围: 153-159
• 页数: 7页
• 分类号: R37

摘要

The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral drugs, information of anatomical therapeutic chemicals is vitally important. In view of this, a CNN based predictor called “iATC_Deep-mISF” has been developed. The predictor is particularly useful in dealing with the multi-label systems in which some chemicals may occur in two or more different classes. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iATC_Deep-mISF/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.