From Black Boxes to Actionable Insights: A Perspective on Explainable Artificial Intelligence for Scientific Discovery-Reference-Cited by-同舟云学术

From Black Boxes to Actionable Insights: A Perspective on Explainable Artificial Intelligence for Scientific Discovery

Published:2023-12-11 Issue:24 Volume:63 Page:7617-7627
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Wu Zhenxing¹²^ORCID,Chen Jihong¹²,Li Yitong¹,Deng Yafeng²,Zhao Haitao³,Hsieh Chang-Yu¹^ORCID,Hou Tingjun¹^ORCID

Affiliation:

1. Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, P. R. China

2. CarbonSilicon AI Technology Company, Limited, Hangzhou, 310018 Zhejiang, P. R. China

3. Center for Intelligent and Biomimetic Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 440305 Guangdong, P. R. China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c01642

Reference64 articles.

1. Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

2. Applications of Deep Learning in Molecule Generation and Molecular Property Prediction