Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies

Retrosynthesis is a technique to plan the chemical synthesisof organic molecules, for example drugs, agro- and fine chemicals. In retrosynthesis, a search treeis 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 learnedfrom 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 searchwith hand-codedheuristics.