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Predicting Reaction Outcomes

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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

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Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit.

Eunjae Shim¹, Ambuj Tewari^2,3, Tim Cernak^1,4

¹Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.

Journal of Chemical Information and Modeling

|June 14, 2023

Summary

Machine learning can predict organic reactions using limited data. Transfer learning and active learning strategies bridge the gap between data-intensive models and expert chemists' low-data approaches for reaction development.

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Area of Science:

Organic Chemistry
Machine Learning
Computational Chemistry

Background:

Machine learning models are increasingly used for predicting organic reaction outcomes.
Current models require large datasets, unlike expert chemists who use limited data.
A gap exists in applying machine learning to low-data scenarios in organic synthesis.

Purpose of the Study:

Introduce active learning and transfer learning strategies.
Connect these strategies to low-data challenges in organic synthesis.
Highlight opportunities for prospective chemical transformation development.

Main Methods:

Review of active learning principles.
Review of transfer learning principles.
Analysis of their applicability to organic synthesis data limitations.

Main Results:

Active and transfer learning can operate effectively with limited reaction data.
These methods offer a pathway to bridge the gap between data-intensive ML and expert intuition.
Potential for advancing prospective chemical reaction development was identified.

Conclusions:

Active and transfer learning are promising for low-data machine learning in organic synthesis.
These strategies can enhance the development of new chemical transformations.
Further research can leverage these methods for real-world synthetic challenges.