Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation
Eike Caldeweyher1, Masha Elkin2, Golsa Gheibi2
1Data Science & Modelling, Pharmaceutical Sciences, R&D, AstraZeneca Gothenburg, SE-431 83 Mölndal, Sweden.
Journal of the American Chemical Society
|July 31, 2023
Summary
Predicting the site of borylation in complex molecules is challenging. A new hybrid computational model, Site of Borylation (SoBo), accurately guides C-H bond functionalization in drug development.
Area of Science:
- Organic Chemistry
- Computational Chemistry
- Chemical Engineering
Background:
- C-H bond borylation is crucial for functionalizing complex molecules.
- Predicting borylation sites in multi-arene systems is difficult with current iridium catalysts.
Purpose of the Study:
- Develop a predictive model for site-selective C-H borylation.
- Improve accuracy in predicting borylation sites for pharmaceutical intermediates.
Main Methods:
- Hybrid computational model combining DFT, QM, cheminformatics, and machine learning.
- Density Functional Theory (DFT) and Semiempirical Quantum Mechanics (QM).
- Machine learning algorithms and linear regression for prediction extrapolation.
Main Results:
- The Site of Borylation (SoBo) model accurately predicts major borylation sites.
- SoBo outperforms previous machine learning models and human experts in accuracy.
- Successful experimental validation on pharmaceutical intermediates.
Conclusions:
- SoBo model offers a powerful tool for guiding experimental C-H borylation.
- Enhances efficiency in pharmaceutical development by predicting specific C(sp2)-H bond functionalization.
- Facilitates site-selective functionalization in complex molecular structures.
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