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.

本篇论文提出了一种新的分子生成模型，模拟了现实中的制备过程，可以通过选择初试反应物（从商业上可获得的分子池中选择），使用反应模型来预测它们之间的反应生成新的分子，同时可以考虑化合物的性质和合成路径的可行性，通过解决反合成问题，预测出一组可以产生目标产品的反应物。