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

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 a

SUBMITTER: Wang X 

PROVIDER: S-EPMC8162445 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature