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Multi-Label Classification Models for the Prediction of Cross-Coupling Reaction Conditions

preprint
submitted on 14.10.2020 and posted on 15.10.2020 by Michael Maser, Alexander Cui, Serim Ryou, Travis DeLano, Yisong Yue, Sarah Reisman

Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Datasets of published reactions were curated for Suzuki, Negishi, and C–N couplings, as well as Pauson–Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each dataset, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph-attention operation in the top-performing model.

History

Email Address of Submitting Author

mmaser@caltech.edu

Institution

California Institute of Technology

Country

United States

ORCID For Submitting Author

0000-0001-7895-7804

Declaration of Conflict of Interest

None

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