Interpretable Molecular Property Predictions Using Marginalized Graph Kernels-Reference-Cited by-同舟云学术

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Interpretable Molecular Property Predictions Using Marginalized Graph Kernels

Published:2023-07-28 Issue:15 Volume:63 Page:4633-4640
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Xiang Yan¹^ORCID,Tang Yu-Hang²^ORCID,Lin Guang³^ORCID,Reker Daniel¹^ORCID

Affiliation:

1. Department of Biomedical Engineering, Duke University, Durham, North Carolina 27705, United States

2. Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States

3. Department of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907, United States

Funder

Duke University

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.3c00396

Reference41 articles.

1. Artificial intelligence in chemistry and drug design

2. Applications of machine learning in drug discovery and development

3. Computationally guided high-throughput design of self-assembling drug nanoparticles

4. Machine Learning Uncovers Food- and Excipient-Drug Interactions

5. Planning chemical syntheses with deep neural networks and symbolic AI

Cited by 1 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. Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure–Activity Relationships;ACS Omega;2024-02-16

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