Automatic Chemical Design is a framework for generating novel molecules with
optimized properties. The original scheme, featuring Bayesian optimization over
the latent space of a variational autoencoder, suffers from the pathology that
it tends to produce invalid molecular structures. First, we demonstrate
empirically that this pathology arises when the Bayesian optimization scheme
queries latent points far away from the data on which the variational
autoencoder has been trained. Secondly, by reformulating the search procedure
as a constrained Bayesian optimization problem, we show that the effects of
this pathology can be mitigated, yielding marked improvements in the validity
of the generated molecules. We posit that constrained Bayesian optimization is
a good approach for solving this class of training set mismatch in many
generative tasks involving Bayesian optimization over the latent space of a
variational autoencoder.

本文提出一种将自动化化学设计概括为使用贝叶斯优化过程，其中包含使用变分自动编码器的潜在空间；进一步将搜索过程转化为约束贝叶斯优化问题，以解决一些根据潜在空间的变分自动编码器所引起的路径问题并且表明其可以在众多生成任务中使用。