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

By Marwin H. S. Segler, Mike Preuss, Mark P. Waller. 2017

35 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Materials Chemistry

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Citations in the corpus

End-to-end Differentiable Proving (2017, methodology): “ate the use of hierarchical attention [25] and reinforcement learning methods such as Monte Carlo tree search [60, 61] that have been used for learning to play Go [62] and chemical synthesis planning [63]. In addition, we plan to support function terms in the future. Based on [64], we are furthermor”
Deep Reinforcement Learning: An Overview (2017, methodology): “We do not discuss some interesting applications, like music generation (Briot et al., 2017; Jaques et al., 2017), and retrosynthesis (Segler et al., 2017).”