Machine Learning Methods for Small Data Challenges in Molecular Science-Reference-Cited by-同舟云学术

Machine Learning Methods for Small Data Challenges in Molecular Science

Published:2023-06-29 Issue:13 Volume:123 Page:8736-8780
ISSN:0009-2665
Container-title:Chemical Reviews
language:en
Short-container-title:Chem. Rev.

Author:

Dou Bozheng¹,Zhu Zailiang¹,Merkurjev Ekaterina²,Ke Lu¹,Chen Long¹,Jiang Jian¹²,Zhu Yueying¹,Liu Jie¹,Zhang Bengong¹,Wei Guo-Wei²³⁴^ORCID

Affiliation:

1. Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China

2. Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States

3. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States

4. Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States

Funder

Division of Mathematical Sciences

National Institute of Allergy and Infectious Diseases

Pfizer

Bristol-Myers Squibb

National Aeronautics and Space Administration

Michigan State University Foundation

National Natural Science Foundation of China

National Institute of General Medical Sciences

Division of Information and Intelligent Systems

Publisher

American Chemical Society (ACS)

Subject

General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.chemrev.3c00189

Reference600 articles.

1. Machine learning: Trends, perspectives, and prospects

2. Campbell, C. Springer Handbook of Bio-/Neuroinformatics; Springer, 2014; pp 185–206.

3. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

4. Chen, M.; Wei, Z.; Huang, Z.; Ding, B.; Li, Y. Simple and deep graph convolutional networks. In Proceedings of the 37th International Conference on Machine Learning, 2020; Vol. 110, pp 1725–1735

Cited by 94 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. How machine learning boosts the understanding of organic pollutant adsorption on carbonaceous materials: A comprehensive review with statistical insights;Separation and Purification Technology;2024-12

2. Top 20 influential AI-based technologies in chemistry;Artificial Intelligence Chemistry;2024-12

3. User-friendly and industry-integrated AI for medicinal chemists and pharmaceuticals;Artificial Intelligence Chemistry;2024-12

4. Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest;Science of The Total Environment;2024-11

5. Advancing allergy source mapping: A comprehensive multidisciplinary framework integrating machine learning, graph theory and game theory;Applied Soft Computing;2024-11