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Generative Modeling to Predict Multiple Suitable Conditions for Chemical Reactions.

Youngchun Kwon^1,2, Sun Kim², Youn-Suk Choi¹

¹Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon16678, Republic of Korea.

Journal of Chemical Information and Modeling

|November 22, 2022

Summary

This study introduces a generative model for predicting multiple reaction conditions in chemical synthesis. The new approach significantly outperforms existing methods in identifying optimal reaction conditions.

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Area of Science:

Computational Chemistry
Machine Learning in Chemistry
Synthetic Chemistry

Background:

Accurate prediction of chemical reaction conditions is crucial for successful synthesis planning.
Machine learning methods have shown promise in recommending reaction elements but offer limited predictions.
Existing models provide single predictions, lacking complete specification of reaction conditions.

Purpose of the Study:

To develop a generative modeling approach for predicting multiple feasible reaction conditions for chemical reactions.
To address the limitations of existing methods by providing complete reaction condition specifications.
To enhance the performance in retrieving ground-truth reaction conditions.

Main Methods:

Formulated reaction condition prediction as sampling from a generative distribution.
Employed a variational autoencoder augmented with a graph neural network.
Trained the model on a reaction dataset, enabling multiple predictions via repeated sampling.

Main Results:

The proposed generative model successfully predicts multiple, fully specified reaction conditions.
Experimental validation on cross-coupling reaction datasets demonstrated superior performance compared to existing methods.
Significantly improved retrieval of ground-truth reaction conditions.

Conclusions:

The generative modeling approach offers a significant advancement in predicting chemical reaction conditions.
This method provides a more comprehensive and accurate solution for synthesis planning.
The approach holds potential for broader applications in computational chemistry and drug discovery.