  Retrosynthesis is a technique to plan the chemical synthesis of organic
molecules, for example drugs, agro- and fine chemicals. In retrosynthesis, a
search tree is built by analysing molecules recursively and dissecting them
into simpler molecular building blocks until one obtains a set of known
building blocks. The search space is intractably large, and it is difficult to
determine the value of retrosynthetic positions. Here, we propose to model
retrosynthesis as a Markov Decision Process. In combination with a Deep Neural
Network policy learned from essentially the complete published knowledge of
chemistry, Monte Carlo Tree Search (MCTS) can be used to evaluate positions. In
exploratory studies, we demonstrate that MCTS with neural network policies
outperforms the traditionally used best-first search with hand-coded
heuristics.
