Prediction of Multicomponent Reaction Yields Using Machine Learning

Main observation and conclusion Prediction of reaction yields using machine learning (ML) can help chemists select high-yielding reactions and provide prior experience before wet-lab experimenting to improve efficiency. However, the exploration of a multicomponent organic reaction features many complex variables and limited number of experimental data, which are challenging for the application of ML. Herein, we perform yield prediction for the synthesis of 2-oxazolidones via Cu-catalyzed radical-type oxy-alkylation of allylamines and herteroarylmethylamines with CO2, which is a three-component reaction. Using physicochemical descriptors as features to launch ML modelling, we find that XGBoost shows significantly improved performance over linear models and these features are effective for the yield prediction. Moreover, out-of-sample prediction indicates the application potential of the model. This study demonstrates great potential of regression-modelling-based ML in organic synthesis even with complex factors and a general small size of reaction data, which are generated from the classical research pattern of method for the inquiry of multicomponent reactions.

Automated summary

Machine learning can help chemists predict reaction yields and select high-yielding reactions to improve experimental efficiency. The study demonstrated the potential of regression modeling in predicting yields for a multicomponent organic reaction, showing improved performance and application potential of the model.