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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
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.
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.
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