CUED Publications database

A model to search for synthesizable molecules

Bradshaw, J and Paige, B and Kusner, MJ and Segler, MHS and Hernández-Lobato, JM (2019) A model to search for synthesizable molecules. Advances in Neural Information Processing Systems, 32. ISSN 1049-5258

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© 2019 Neural information processing systems foundation. All rights reserved. Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models allow one to generate molecules with desirable properties, they give no guarantees that the molecules can actually be synthesized in practice. We propose a new molecule generation model, mirroring a more realistic real-world process, where (a) reactants are selected, and (b) combined to form more complex molecules. More specifically, our generative model proposes a bag of initial reactants (selected from a pool of commercially-available molecules) and uses a reaction model to predict how they react together to generate new molecules. We first show that the model can generate diverse, valid and unique molecules due to the useful inductive biases of modeling reactions. Furthermore, our model allows chemists to interrogate not only the properties of the generated molecules but also the feasibility of the synthesis routes. We conclude by using our model to solve retrosynthesis problems, predicting a set of reactants that can produce a target product.

Item Type: Article
Uncontrolled Keywords: cs.LG cs.LG physics.comp-ph stat.ML
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 02 Jul 2019 01:09
Last Modified: 17 Sep 2020 02:57