A Model to Search for Synthesizable Molecules

TL;DR

This paper introduces a new molecule generation model that mimics real-world chemical synthesis by selecting reactants and predicting their reactions, enabling the generation of synthesizable molecules and solving retrosynthesis problems.

Contribution

A novel generative model that incorporates reaction-based synthesis processes, improving the realism and practicality of generated molecules.

Findings

Model generates diverse, valid molecules due to reaction-based biases.

Allows assessment of synthesis feasibility for generated molecules.

Successfully predicts reactants for target molecules in retrosynthesis tasks.

Abstract

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.