Chemprop: A Machine Learning Package for Chemical Property Prediction-Reference-Cited by-同舟云学术

Chemprop: A Machine Learning Package for Chemical Property Prediction

Published:2023-12-26 Issue:1 Volume:64 Page:9-17
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Heid Esther¹²^ORCID,Greenman Kevin P.¹^ORCID,Chung Yunsie¹^ORCID,Li Shih-Cheng¹³,Graff David E.¹⁴^ORCID,Vermeire Florence H.¹⁵,Wu Haoyang¹^ORCID,Green William H.¹^ORCID,McGill Charles J.¹⁶^ORCID

Affiliation:

1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

2. Institute of Materials Chemistry, TU Wien, 1060 Vienna, Austria

3. Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan

4. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States

5. Department of Chemical Engineering, KU Leuven, Celestijnenlaan 200F, B-3001 Leuven, Belgium

6. Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, Virginia 23284, United States

Funder

Defense Sciences Office, DARPA

KU Leuven

Austrian Science Fund

Division of Graduate Education

Massachusetts Institute of Technology

Virginia Commonwealth University

MIT-IBM Watson Lab

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.3c01250

Reference87 articles.

1. Gasteiger, J.; Groß, J.; Günnemann, S. Directional Message Passing for Molecular Graphs. Proceedings of the International Conference on Learning Representations, 2003, arXiv:2003.03123.

2. Zhang, S.; Liu, Y.; Xie, L. Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures. Machine Learning for Molecules Workshop at NeurIPS, 2020, arXiv:2011.07457.

3. Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures

4. Machine Learning for Physicochemical Property Prediction of Complex Hydrocarbon Mixtures

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