  In applications such as molecule design or drug discovery, it is desirable to
have an algorithm which recommends new candidate molecules based on the results
of past tests. These molecules first need to be synthesized and then tested for
objective properties. We describe ChemBO, a Bayesian optimization framework for
generating and optimizing organic molecules for desired molecular properties.
While most existing data-driven methods for this problem do not account for
sample efficiency or fail to enforce realistic constraints on synthesizability,
our approach explores the synthesis graph in a sample-efficient way and
produces synthesizable candidates. We implement ChemBO as a Gaussian process
model and explore existing molecular kernels for it. Moreover, we propose a
novel optimal-transport based distance and kernel that accounts for graphical
information explicitly. In our experiments, we demonstrate the efficacy of the
proposed approach on several molecular optimization problems.
