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Chemical reactions often occur in a stepwise fashion involving two or more distinct reactions taking place in a sequence. A balanced equation indicates the reacting species and the product species, but it reveals no details about how the reaction occurs at the molecular level. The reaction mechanism (or reaction path) provides details regarding the precise, step-by-step process by which a reaction occurs. Each of the steps in a reaction mechanism is called an elementary reaction. These...
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Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
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Data-driven approaches for identifying hyperparameters in multi-step retrosynthesis.

Annie M Westerlund1, Bente Barge1,2, Lewis Mervin3

  • 1Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.

Molecular Informatics
|September 7, 2023
PubMed
Summary
This summary is machine-generated.

Optimizing Monte Carlo tree search (MCTS) hyperparameters improves multistep retrosynthesis performance. Data-driven approaches identified settings that enhance route solvability and reduce search time, outperforming current defaults.

Keywords:
AiZynthFinderMCTSMonte Carlo tree searchOptunahyperparameter optimizationmachine learningretrosynthesis

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Area of Science:

  • Computational Chemistry
  • Artificial Intelligence in Chemistry
  • Retrosynthesis Planning

Background:

  • Multistep retrosynthesis is crucial for drug discovery and chemical synthesis.
  • The efficiency of retrosynthesis planning algorithms, like Monte Carlo Tree Search (MCTS), is highly dependent on hyperparameter tuning.
  • Balancing search time and route solvability is a key challenge in computational retrosynthesis.

Purpose of the Study:

  • To investigate the impact of MCTS hyperparameters on multistep retrosynthesis performance.
  • To identify optimal MCTS hyperparameter settings for improved speed and accuracy in retrosynthesis.
  • To compare data-driven hyperparameter optimization strategies against default settings.

Main Methods:

  • Evaluated MCTS hyperparameters (iterations, depth, width) using systematic grid search, Bayesian optimization, and a machine learning approach.
  • Assessed performance using Linear Integrated Speed-Accuracy Score (LISAS) and Inverse Efficiency Score.
  • Tested optimized hyperparameters on proprietary and public datasets (ChEMBL).

Main Results:

  • Identified a hyperparameter set that significantly outperforms the default AiZynthFinder settings.
  • Achieved 93% solvability with a 151s median search time on an in-house dataset.
  • Reached 74% solvability with a 114s median search time on the ChEMBL dataset, surpassing default performance.

Conclusions:

  • Data-driven hyperparameter optimization effectively enhances MCTS performance for multistep retrosynthesis.
  • The identified optimal settings provide a superior balance of speed and solvability for retrosynthesis planning.
  • Dynamic hyperparameter prediction shows promise for real-time optimization in chemical synthesis.