  In the last few years, de novo molecular design using machine learning has
made great technical progress but its practical deployment has not been as
successful. This is mostly owing to the cost and technical difficulty of
synthesizing such computationally designed molecules. To overcome such
barriers, various methods for synthetic route design using deep neural networks
have been studied intensively in recent years. However, little progress has
been made in designing molecules and their synthetic routes simultaneously.
Here, we formulate the problem of simultaneously designing molecules with the
desired set of properties and their synthetic routes within the framework of
Bayesian inference. The design variables consist of a set of reactants in a
reaction network and its network topology. The design space is extremely large
because it consists of all combinations of purchasable reactants, often in the
order of millions or more. In addition, the designed reaction networks can
adopt any topology beyond simple multistep linear reaction routes. To solve
this hard combinatorial problem, we present a powerful sequential Monte Carlo
algorithm that recursively designs a synthetic reaction network by sequentially
building up single-step reactions. In a case study of designing drug-like
molecules based on commercially available compounds, compared with heuristic
combinatorial search methods, the proposed method shows overwhelming
performance in terms of computational efficiency and coverage and novelty with
respect to existing compounds.
