International Journal of New Chemistry
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Machine Learning Approaches in Predicting Chemical Reactions: A Comprehensive Review

Document Type : Review

Authors

Hossein Tarahomi Ardakani 1
Hamid Reza Hanif 2
Mohsen Katebi Jahromi 3

1 Department of Computer Science, Payame Noor University (PNU), Tehran, Iran.

2 Department of Mathematics and Computer Science, Iran University of Science and Technology

3 Department of Electrical Engineering, Safashahr Branch, Islamic Azad University, Safashahr, Iran.

10.22034/ijnc.2025.721979
Abstract
The rapid advancement of machine learning (ML) techniques has significantly transformed the landscape of chemical research, particularly in predicting chemical reactions. This review provides a comprehensive overview of the various machine learning approaches utilized in the prediction of reaction outcomes, mechanisms, and kinetics. We begin by discussing the foundational concepts of machine learning and its relevance to chemistry, highlighting key algorithms such as neural networks, support vector machines, and decision trees. The paper systematically categorizes existing methodologies based on their application: reaction outcome prediction, reaction mechanism elucidation, and kinetic modeling. We delve into the datasets commonly employed for training ML models, emphasizing the importance of high-quality, curated chemical data. Furthermore, we explore the integration of quantum chemical calculations with machine learning to enhance predictive accuracy. Challenges such as data sparsity, model interpretability, and the need for generalizability across diverse chemical spaces are critically examined. Finally, we discuss future directions for research, including the incorporation of transfer learning, active learning, and the development of user-friendly software tools to democratize access to these powerful predictive techniques. This review aims to provide a valuable resource for chemists and data scientists alike, fostering collaboration and innovation at the intersection of chemistry and artificial intelligence.

Keywords

Machine Learning
Chemical Reactions
Prediction Models
Data-Driven Approaches
Subjects
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International Journal of New Chemistry
Volume 12, Issue 4
Winter 2025
Pages 874-889

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