Prediction of Organic Reaction Outcomes Using Machine Learning
Connor W Coley1, Regina Barzilay1, Tommi S Jaakkola1
1Department of Chemical Engineering and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
ACS Central Science
|June 3, 2017
Summary
This study introduces a new AI model for predicting chemical synthesis outcomes, improving the accuracy of computer-aided retrosynthesis planning. The model successfully identifies the correct major product in over 70% of cases, enhancing experimental success rates.
Area of Science:
- Computational Chemistry
- Artificial Intelligence in Chemistry
- Organic Synthesis
Background:
- Computer-assisted retrosynthesis planning tools have limited adoption due to unreliable reaction outcome predictions.
- A key challenge is the discrepancy between predicted and experimentally observed reaction results.
Purpose of the Study:
- To develop a novel model framework for accurately anticipating chemical reaction outcomes.
- To improve the reliability and adoption of computer-aided synthesis design software.
Main Methods:
- A hybrid approach combining reaction templates with neural network pattern recognition.
- Training a model on 15,000 experimental reaction records from US patents.
- Utilizing a unique edit-based representation for candidate reactions.
Main Results:
- The model achieved a rank 1 prediction for the major product in 71.8% of cases.
- Rank ≤3 and rank ≤5 predictions were achieved in 86.7% and 90.8% of cases, respectively.
- Demonstrated high accuracy in identifying experimentally observed major products.
Conclusions:
- The developed model framework significantly enhances the prediction accuracy of chemical reaction outcomes.
- This approach offers a promising solution to improve the reliability of retrosynthesis planning software.
- The findings pave the way for more effective computer-aided design in chemical synthesis.
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