Uncertainty Quantification Using Neural Networks for Molecular Property Prediction-Reference-Cited by-同舟云学术

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Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

Published:2020-07-23 Issue:8 Volume:60 Page:3770-3780
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Hirschfeld Lior¹,Swanson Kyle²^ORCID,Yang Kevin³,Barzilay Regina¹,Coley Connor W.⁴^ORCID

Affiliation:

1. Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts 02139, United States

2. Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge CB3 0WB, U.K.

3. Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, California 94720, United States

4. Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States

Funder

U.S. Department of Defense

Machine Learning for Pharmaceutical Discovery and Synthesis consortium

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.0c00502

Reference36 articles.

1. QSAR Modeling: Where Have You Been? Where Are You Going To?

2. QSAR without borders

3. Methods for comparing uncertainty quantifications for material property predictions

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