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Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits

Cite this: J. Chem. Inf. Model. 2021, 61, 7, 3273–3284
Publication Date (Web):July 12, 2021
https://doi.org/10.1021/acs.jcim.1c00537
Copyright © 2021 American Chemical Society
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    Abstract

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    The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely synthesis pathway cannot be applied due to additional constraints, which requires proposing alternative chemical reactions. With this in mind, we present Molecule Edit Graph Attention Network (MEGAN), an end-to-end encoder–decoder neural model. MEGAN is inspired by models that express a chemical reaction as a sequence of graph edits, akin to the arrow pushing formalism. We extend this model to retrosynthesis prediction (predicting substrates given the product of a chemical reaction) and scale it up to large data sets. We argue that representing the reaction as a sequence of edits enables MEGAN to efficiently explore the space of plausible chemical reactions, maintaining the flexibility of modeling the reaction in an end-to-end fashion and achieving state-of-the-art accuracy in standard benchmarks. Code and trained models are made available online at https://github.com/molecule-one/megan.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.1c00537.

    • Detailed description of the featurization used in the MEGAN model, list of graph actions used in experiments on the USPTO-50k data set, description of the algorithm used to break ties when determining the ground truth sequence of actions in training of the MEGAN model, and values of hyperparameters used in the experiments (PDF)

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