Analyzing Learned Molecular Representations for Property Prediction

Author:

Yang Kevin1,Swanson Kyle1ORCID,Jin Wengong1,Coley Connor2ORCID,Eiden Philipp3,Gao Hua4,Guzman-Perez Angel4,Hopper Timothy4,Kelley Brian5,Mathea Miriam3,Palmer Andrew3,Settels Volker3,Jaakkola Tommi1,Jensen Klavs2ORCID,Barzilay Regina1

Affiliation:

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

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

3. BASF SE, Ludwigshafen 67063, Germany

4. Amgen Inc., Cambridge, Massachusetts 02141, United States

5. Novartis Institutes for BioMedical Research, Cambridge, Massachusetts 02139, United States

Funder

Novartis

BASF

Defense Advanced Research Projects Agency

Massachusetts Institute of Technology

Amgen

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

Reference65 articles.

1. MoleculeNet: a benchmark for molecular machine learning

2. Molecular graph convolutions: moving beyond fingerprints

3. Li, Y.; Tarlow, D.; Brockschmidt, M.; Zemel, R. Gated Graph Sequence Neural Networks. 2015, arXiv preprint arXiv:1511.05493. https://arxiv.org/abs/1511.05493 (accessed Aug 6, 2019).

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