Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks-Reference-Cited by-同舟云学术

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Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks

Published:2024-07-02 Issue:14 Volume:64 Page:5439-5450
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Gale-Day Zachary J.¹²^ORCID,Shub Laura¹³²⁴^ORCID,Chuang Kangway V.¹³²⁴^ORCID,Keiser Michael J.¹³²⁴^ORCID

Affiliation:

1. Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States

2. Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, California 94158, United States

3. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94158, United States

4. Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, California 94158, United States

Funder

Chan Zuckerberg Initiative

Sandler Foundation

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00311

Reference57 articles.

1. Machine learning models for drug–target interactions: current knowledge and future directions

2. Deep learning in drug discovery: an integrative review and future challenges

3. Applications of machine learning in drug discovery and development

4. Machine Learning in Drug Discovery: A Review

5. On the Frustration to Predict Binding Affinities from Protein–Ligand Structures with Deep Neural Networks

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