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The reverse of the aldol addition reaction is called the retro-aldol reaction. Here, the carbon–carbon bond in the aldol product is cleaved under acidic or basic conditions to form two molecules of carbonyl compounds. The mechanism of the reaction consists of three steps.
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Bayesian Algorithm for Retrosynthesis.

Zhongliang Guo1, Stephen Wu1,2, Mitsuru Ohno3

  • 1The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo 190-8562, Japan.

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|September 25, 2020
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Summary
This summary is machine-generated.

Machine learning accelerates chemical synthesis by predicting reaction pathways. This Bayesian retrosynthesis approach efficiently discovers numerous synthetic routes for target molecules, aiding chemists in planning complex syntheses.

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Area of Science:

  • Organic Chemistry
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Retrosynthetic planning is traditionally time-consuming and relies heavily on expert knowledge.
  • Limited exploration of the vast chemical reaction space hinders efficient synthesis discovery.
  • Machine learning offers a transformative approach to chemical retrosynthesis.

Purpose of the Study:

  • To develop a machine learning-driven method for discovering synthetic routes.
  • To efficiently explore the combinatorial complexity of retrosynthetic planning.
  • To identify diverse and probable reaction pathways from target molecules to commercially available compounds.

Main Methods:

  • Utilized a deep neural network for forward prediction of reaction products from reactants.
  • Inverted the forward model into a backward model using Bayesian inference.
  • Employed a Monte Carlo search algorithm to explore diverse reaction sequences.

Main Results:

  • Achieved approximately 87% accuracy in forward model predictions.
  • Successfully rediscovered 81.8% of known one-step and 33.3% of two-step synthetic routes with top-10 accuracy.
  • Generated hundreds of diverse reaction routes for synthetic targets.

Conclusions:

  • The Bayesian retrosynthesis algorithm significantly enhances the efficiency of synthetic route discovery.
  • The method provides a diverse set of potential reaction pathways, aiding in complex synthesis planning.
  • This approach integrates computational methods with expert chemical knowledge for improved retrosynthesis.