期刊
CHEM
卷 6, 期 7, 页码 1527-1542出版社
CELL PRESS
DOI: 10.1016/j.chempr.2020.05.014
关键词
-
资金
- National Institutes of Health, United States [5U01GM121667]
- Belgian Foundation of Scientific Research-Flanders (FWO) postdoctoral fellowship
- Office of Science of the US Department of Energy [DE-AC02-05CH11231]
Recently, supervised machine learning has been ascending in providing new predictive approaches for chemical, biological, and materials sciences applications. In this Perspective, we focus on the interplay of machine learning methods with the chemically motivated descriptors and the size and type of datasets needed for molecular property prediction. Using nuclear magnetic resonance chemical shift prediction as an example, we demonstrate that success is predicated on the choice of feature extracted or real-space representations of chemical structures, whether the molecular property data are abundant and/or experimentally or computationally derived, and how these together will influence the correct choice of popular machine learning methods drawn from deep learning, random forests, or kernel methods.
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