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Published on: November 11, 2008
Evaluation of Machine Learning Models for Condition Optimization in Diverse Amide Coupling Reactions
Abhinav Sai Chalasani1, Sourodeep Deb1, Aarav Anand1
1Department of Chemistry, Biochemistry & Physics, Aspiring Scholars Directed Research Program, Fremont, California 94539, United States.
ACS Omega
|July 24, 2026
Summary
Machine learning models can predict ideal coupling agents for amide coupling reactions, achieving 87% accuracy. While yield prediction remains challenging, this approach streamlines synthetic chemistry by classifying reactions effectively.
Area of Science:
- Computational Chemistry
- Machine Learning in Chemistry
- Synthetic Organic Chemistry
Background:
- Optimizing chemical reactions, especially amide couplings, is resource-intensive due to substrate-specific electronic and steric properties.
- Amide couplings are crucial in medicinal chemistry, representing ~40% of synthetic transformations, but are challenging for predictive modeling.
- Existing reaction data requires standardization for effective machine learning application.
Purpose of the Study:
- To develop a platform for standardizing and filtering open-source reaction data from the Open Reaction Database (ORD).
- To evaluate the performance of 13 machine learning models for predicting amide coupling reaction yields and classifying coupling agents.
- To assess the impact of molecular environment features versus bulk properties on model predictivity.
Main Methods:
- Standardized and filtered 3800 amide coupling reactions from the ORD.
- Evaluated 13 machine learning models (linear, tree-based, kernel, instance-based, neural network, ensemble) for yield prediction and coupling agent classification.
- Utilized molecular environment features (XYZ coordinates, 3D features, Morgan fingerprints) and bulk properties (SMILES-derived features) as model inputs.
Main Results:
- Yield prediction achieved moderate success with R² scores of 0.61 due to data complexity.
- Coupling agent classification showed high performance, with ensemble and kernel-based models reaching 87% accuracy.
- Molecular environment features significantly improved model predictivity compared to bulk material properties.
Conclusions:
- Machine learning models, particularly kernel and ensemble methods, are effective for classifying ideal coupling agents in amide coupling reactions.
- The developed platform standardizes reaction data for robust machine learning evaluation.
- 3D molecular environment features are more predictive than 1D/2D properties for optimizing amide coupling reactions.
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