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Poster
A Model to Search for Synthesizable Molecules
John Bradshaw · Brooks Paige · Matt Kusner · Marwin Segler · José Miguel Hernández-Lobato

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #109
in Deep Learning -- Generative Models »

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.

Keywords: Generative Models · Computational Biology and Bioinformatics · Applications · Deep Learning
[ Paper]  [ Poster] 

Author Information

John Bradshaw (University of Cambridge/MPI IS Tübingen)
Brooks Paige (Alan Turing Institute)
Matt Kusner (University College London)
Marwin Segler (BenevolentAI)
José Miguel Hernández-Lobato (University of Cambridge)

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