AMPL: A Data-Driven Modeling Pipeline for Drug Discovery-Reference-Cited by-同舟云学术

AMPL: A Data-Driven Modeling Pipeline for Drug Discovery

Published:2020-04-03 Issue:4 Volume:60 Page:1955-1968
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Minnich Amanda J.¹^ORCID,McLoughlin Kevin¹^ORCID,Tse Margaret²,Deng Jason²,Weber Andrew²,Murad Neha²,Madej Benjamin D.³,Ramsundar Bharath⁴,Rush Tom²,Calad-Thomson Stacie²,Brase Jim¹,Allen Jonathan E.¹

Affiliation:

1. Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States

2. GlaxoSmithKline, 5 Crescent Drive Philadelphia Pennsylvania 19112, United States

3. Frederick National Laboratory, 8560 Progress Drive, Frederick, Maryland 21701, United States

4. Computable, San Francisco, California 94111, United States

Funder

National Cancer Institute

Lawrence Livermore National Laboratory

GlaxoSmithKline

National Nuclear Security Administration

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.9b01053

Reference25 articles.

1. Dahl, G. E.; Jaitly, N.; Salakhutdinov, R. Multi-task Neural Networks for QSAR Predictions. arXiv:1406.1231v12014; pp 1–21.

2. Feinberg, E. N.; Sheridan, R.; Joshi, E.; Pande, V. S.; Cheng, A. C. Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurization. arXiv:1903.11789 [cs, stat] 2019.

3. Analyzing Learned Molecular Representations for Property Prediction

4. Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology: deepchem/deepchem. 2019-07-31; https://github.com/deepchem/deepchem.

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