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The rate-determining step, or RDS, in a chemical reaction is the slowest step that determines the overall reaction rate. It is identified by using the observed rate law and typically involves approximation methods like the RDS approximation or the steady-state approximation.In the RDS approximation, also known as the rate-limiting-step or equilibrium approximation, the reaction mechanism consists of one or more reversible reactions near equilibrium, followed by a slower RDS, and then one or...
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Quantifying the failure modes of current one-step retrosynthesis models.

Suong B A Tran1, Jihye Roh2, Connor W Coley2,3

  • 1Department of Chemistry, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA.

Chemical Science
|June 15, 2026
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Summary

Computer-aided synthesis planning (CASP) models struggle to replicate literature synthesis pathways. This study quantifies their failures, revealing biases toward simpler reactions and opportunities for improved retrosynthesis prediction.

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Area of Science:

  • Computational Chemistry
  • Organic Synthesis
  • Artificial Intelligence in Chemistry

Background:

  • Computer-aided synthesis planning (CASP) tools automate retrosynthetic analysis using one-step models.
  • These tools often fail to reproduce experimentally validated synthesis pathways found in literature.
  • Failures stem from missed precursors or poor ranking of proposed steps.

Purpose of the Study:

  • To quantitatively analyze the challenges faced by data-driven one-step retrosynthesis models.
  • To identify specific failure modes in reproducing literature-reported precursors.
  • To provide insights for improving CASP models and their application in prospective synthesis planning.

Main Methods:

  • Evaluation of model performance using top-k exact-match accuracy.
  • Stratification of accuracy based on product and reaction complexity.
  • Analysis of prediction biases (e.g., reacting atoms, ring changes).
  • Assessment using complementary metrics for stereochemistry, leaving groups, and multi-stage reactions.

Main Results:

  • Model performance decreases significantly with increasing reaction and product complexity.
  • Models systematically underpredict the complexity of transformations (reacting atoms, ring changes).
  • A bias towards simpler transformations was observed, even with complex data in training sets.
  • Complementary metrics revealed further limitations in handling stereochemistry and multi-step reactions.

Conclusions:

  • Data-driven one-step retrosynthesis models exhibit limitations in capturing complex, literature-reported chemical reactions.
  • Performance degradation with complexity highlights a need for models that better handle intricate transformations.
  • The findings offer guidance for enhancing future CASP models and optimizing the use of their predictions in chemical synthesis planning.