Machine learning-guided strategies for reaction conditions design and optimization
Journal
Beilstein Journal of Organic Chemistry
Journal Volume
20
Start Page
2476
End Page
2492
ISSN
1860-5397
Date Issued
2024-10-04
Author(s)
Lung-Yi Chen
DOI
10.3762/bjoc.20.212
Abstract
This review surveys the recent advances and challenges in predicting and optimizing reaction conditions using machine learning techniques. The paper emphasizes the importance of acquiring and processing large and diverse datasets of chemical reactions, and the use of both global and local models to guide the design of synthetic processes. Global models exploit the information from comprehensive databases to suggest general reaction conditions for new reactions, while local models fine-tune the specific parameters for a given reaction family to improve yield and selectivity. The paper also identifies the current limitations and opportunities in this field, such as the data quality and availability, and the integration of high-throughput experimentation. The paper demonstrates how the combination of chemical engineering, data science, and ML algorithms can enhance the efficiency and effectiveness of reaction conditions design, and enable novel discoveries in synthetic chemistry.
Subjects
data preprocessing
reaction conditions prediction
reaction data mining
reaction optimization
reaction representation
SDGs
Publisher
Beilstein Institut
Type
journal article