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Using Machine Learning To Predict Suitable Conditions for Organic Reactions.

Hanyu Gao¹, Thomas J Struble¹, Connor W Coley¹

¹Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.

ACS Central Science

|December 18, 2018

Summary

This summary is machine-generated.

A new neural network model predicts optimal reaction conditions for organic synthesis, improving computer-assisted planning. It accurately suggests catalysts, solvents, reagents, and temperatures, aiding experimental validation and success rates.

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Area of Science:

Organic Chemistry
Computational Chemistry
Machine Learning

Background:

Computer-assisted synthetic planning requires accurate reaction condition recommendations.
De novo condition recommendation is challenging, relying heavily on expert knowledge.
Optimizing reaction conditions is crucial for experimental success.

Purpose of the Study:

To develop a neural-network model for predicting optimal organic reaction conditions.
To provide accurate suggestions for catalysts, solvents, reagents, and temperature.
To enhance computer-assisted synthetic planning and experimental validation.

Main Methods:

Development of a neural-network model trained on approximately 10 million examples from Reaxys.
Prediction of chemical context (catalyst, solvent, reagent) and temperature for organic reactions.
Evaluation of model performance based on top-10 prediction accuracy and temperature prediction within a specified range.

Main Results:

The model achieved 69.6% accuracy in predicting the correct catalyst, solvent, and reagent within the top-10 suggestions.
Top-10 accuracies for individual species (catalyst, solvent, reagent) reached 80-90%.
Temperature was predicted within ±20 °C of the recorded temperature in 60-70% of test cases, with higher accuracy when the chemical context was correctly predicted.

Conclusions:

The developed neural-network model effectively predicts suitable reaction conditions for organic synthesis.
The model demonstrates utility across various common reaction classes.
The model implicitly learns functional similarities between solvent and reagent species through continuous numerical embeddings.