Methyl Cation Affinity and Methyl Anion Affinity Prediction Using Uni-Mol-Based Models
Yuto Iwasaki1, Akinori Sato1,2, Tomoyuki Miyao1,2
1Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.
The Journal of Physical Chemistry. A
|June 16, 2026
Summary
Accurate machine learning models now predict organic compound reactivity using methyl cation affinity (MCA) and methyl anion affinity (MAA). These models achieve high accuracy and speed, overcoming previous limitations for synthetic chemistry applications.
Area of Science:
- Computational chemistry
- Machine learning in chemistry
- Organic reaction prediction
Background:
- Predicting nucleophilicity and electrophilicity is vital for designing polar reactions in organic chemistry.
- Methyl cation affinity (MCA) and methyl anion affinity (MAA) are key indicators, but current machine learning models lack sufficient accuracy.
- Existing surrogate models for MCA and MAA calculations are computationally expensive and not practical for widespread use.
Purpose of the Study:
- To develop highly accurate and efficient machine learning surrogate models for predicting methyl cation affinity (MCA) and methyl anion affinity (MAA).
- To improve the prediction accuracy of nucleophilicity and electrophilicity indicators for organic compounds.
- To enable fast and reliable reactivity predictions for synthetic chemistry applications.
Main Methods:
- Developed machine learning surrogate models utilizing a pretrained Uni-Mol encoder block and a feed-forward neural network.
- Input conformations for MCA and MAA calculations were investigated for their impact on prediction accuracy.
- Evaluated model performance against existing molecular graph-based neural network models and robustness to different calculation protocols.
Main Results:
- Achieved low root-mean-square errors: 8.90 kJ/mol for MCA and 10.02 kJ/mol for MAA.
- The proposed architecture demonstrated superior performance compared to graph-based models, even without extensive pretraining.
- Inference time was reduced to less than 0.1 seconds per compound on a single GPU.
- Models showed robustness across different MCA/MAA calculation protocols and effectiveness with simple fine-tuning for data scarcity.
Conclusions:
- The developed machine learning models offer a significant advancement in accurately and rapidly predicting MCA and MAA.
- The architectural superiority and efficiency of the models make them practical tools for synthetic chemists.
- These models overcome data scarcity issues and provide a robust solution for fast reactivity prediction in organic chemistry.
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