Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search
- URL: http://arxiv.org/abs/2112.06028v2
- Date: Sat, 10 Jun 2023 03:13:46 GMT
- Title: Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search
- Authors: Siqi Hong, Hankz Hankui Zhuo, Kebing Jin, Guang Shao, Zhanwen Zhou
- Abstract summary: In retrosynthetic planning, the huge number of possible routes to synthesize a complex molecule leads to an explosion of possibilities.
Current approaches rely on human-defined or machine-trained score functions which have limited chemical knowledge.
We build an experience guidance network to learn knowledge from synthetic experiences during the search.
- Score: 10.67810457039541
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In retrosynthetic planning, the huge number of possible routes to synthesize
a complex molecule using simple building blocks leads to a combinatorial
explosion of possibilities. Even experienced chemists often have difficulty to
select the most promising transformations. The current approaches rely on
human-defined or machine-trained score functions which have limited chemical
knowledge or use expensive estimation methods for guiding. Here we an propose
experience-guided Monte Carlo tree search (EG-MCTS) to deal with this problem.
Instead of rollout, we build an experience guidance network to learn knowledge
from synthetic experiences during the search. Experiments on benchmark USPTO
datasets show that, EG-MCTS gains significant improvement over state-of-the-art
approaches both in efficiency and effectiveness. In a comparative experiment
with the literature, our computer-generated routes mostly matched the reported
routes. Routes designed for real drug compounds exhibit the effectiveness of
EG-MCTS on assisting chemists performing retrosynthetic analysis.
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