Predicting Regioselectivity in Radical C-H Functionalization of Heterocycles through Machine Learning

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Machine Learning

Background:

• Radical C-H bond functionalization is key for synthesizing complex heterocyclic compounds.
• Predicting regioselectivity in these reactions is crucial but remains a significant challenge.
• Existing methods for regioselectivity prediction lack quantification and efficiency.

Purpose of the Study:

• To develop a rapid and reliable method for predicting regioselectivity in radical C-H bond functionalization of heterocycles.
• To demonstrate the feasibility of using machine learning models for this prediction task.
• To identify key computational features that govern regioselectivity.

Main Methods:

• Utilized a machine learning approach, specifically a Random Forest model.
• Input features were derived from computed properties of isolated reactants.
• Model performance was evaluated using site and selectivity accuracy metrics on an out-of-sample test set.

Main Results:

• The Random Forest model achieved high accuracy: 94.2% site accuracy and 89.9% selectivity accuracy.
• The model successfully predicted regioselectivity for various heterocycles and substituents.
• Validation against experimental data confirmed the model's predictive power.

Conclusions:

• Machine learning models, trained on physical organic features, can accurately predict regioselectivity in radical C-H functionalization.
• This approach offers a significant improvement over existing methods for selectivity prediction.
• Combining computational statistics with machine learning provides a powerful strategy for optimizing organic transformations.