Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning.

Chemical Science
Xiaoxue WangKlavs F Jensen

Abstract

Computer aided synthesis planning of synthetic pathways with green process conditions has become of increasing importance in organic chemistry, but the large search space inherent in synthesis planning and the difficulty in predicting reaction conditions make it a significant challenge. We introduce a new Monte Carlo Tree Search (MCTS) variant that promotes balance between exploration and exploitation across the synthesis space. Together with a value network trained from reinforcement learning and a solvent-prediction neural network, our algorithm is comparable to the best MCTS variant (PUCT, similar to Google's Alpha Go) in finding valid synthesis pathways within a fixed searching time, and superior in identifying shorter routes with greener solvents under the same search conditions. In addition, with the same root compound visit count, our algorithm outperforms the PUCT MCTS by 16% in terms of determining successful routes. Overall the success rate is improved by 19.7% compared to the upper confidence bound applied to trees (UCT) MCTS method. Moreover, we improve 71.4% of the routes proposed by the PUCT MCTS variant in pathway length and choices of green solvents. The approach generally enables including Green Chemistry consi...Continue Reading

References

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Citations

Jul 20, 2021·Current Opinion in Chemical Biology·Jean-Loup Faulon, Léon Faure
Jul 22, 2021·ACS Applied Materials & Interfaces·Suvo BanikSubramanian K R S Sankaranarayanan
Sep 15, 2021·Chemical Society Reviews·Jana M WeberAlexei A Lapkin
Sep 28, 2021·Briefings in Bioinformatics·Jingxin DongXiangxiang Zeng

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Software Mentioned

ASKCOS
Retro
Reaxys
MCTS
CASP
UCT
PUCT MCTS
dc
mUCT
PUCT

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