General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps-Reference-Cited by-同舟云学术

General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps

Published:2021-01-26 Issue:2 Volume:61 Page:603-620
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Brown Benjamin P.¹^ORCID,Mendenhall Jeffrey²,Geanes Alexander R.²,Meiler Jens²³⁴

Affiliation:

1. Chemical and Physical Biology Program, Medical Scientist Training Program, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States

2. Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, Tennessee 37232, United States

3. Departments of Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, Tennessee 37212, United States

4. Institute for Drug Discovery, Leipzig University Medical School, Leipzig SAC 04103, Germany

Funder

National Institute of Diabetes and Digestive and Kidney Diseases

National Institutes of Health

Publisher

American Chemical Society (ACS)

Subject

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

Link

http://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c01001

Reference61 articles.

1. Computational methods in drug discovery

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3. Butkiewicz, M.; Mueller, R.; Selic, D.; Dawson, E.; Meiler, J. In Application of Machine Learning Approaches on Quantitative Structure Activity Relationships, IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, 2009.

4. Dahl, G. E. Multi-Task Neural Networks for Qsar Predictions. 2014, arXiv:1406.1231. arXiv.org e-Print archive. https://arxiv.org/abs/1406.1231.

5. Improving quantitative structure–activity relationship models using Artificial Neural Networks trained with dropout

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