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Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

Ryou, Serim and Maser, Michael R. and Cui, Alexander Y. and DeLano, Travis J. and Yue, Yisong and Reisman, Sarah E. (2020) Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20201110-154207213

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Abstract

We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2007.04275arXivDiscussion Paper
ORCID:
AuthorORCID
Maser, Michael R.0000-0001-7895-7804
DeLano, Travis J.0000-0002-2052-611X
Yue, Yisong0000-0001-9127-1989
Reisman, Sarah E.0000-0001-8244-9300
Additional Information:© 2020 by the author(s). To appear in the ICML 2020 Workshop on Graph Representation Learning and Beyond (GRLB). We thank the reviewers for their insightful comments and Prof Pietro Perona for mentorship guidance and helpful discussions on this work. Fellowship support was provided by the NSF (M.R.M., T.J.D. Grant No. DGE-1144469). S.E.R. is a Heritage Medical Research Investigator. Financial support from the Research Corporation Cottrell Scholars Program is acknowledged.
Group:Heritage Medical Research Institute
Funders:
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-1144469
Heritage Medical Research InstituteUNSPECIFIED
Cottrell Scholar of Research CorporationUNSPECIFIED
Record Number:CaltechAUTHORS:20201110-154207213
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201110-154207213
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106598
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:11 Nov 2020 00:08
Last Modified:25 Jun 2021 21:40

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