Predicting and Explaining Yields with Machine Learning for Carboxylated Azoles and Beyond.

Abstract

Carbon dioxide (CO₂) can be transformed into valuable chemical building blocks, including C2-carboxylated 1,3-azoles, which have potential applications in pharmaceuticals, cosmetics, and pesticides. However, only a small fraction of the millions of available 1,3-azoles are carboxylated at the C2 position, highlighting significant opportunities for further research in the synthesis and application of these compounds. In this study, we utilized a supervised machine learning approach to predict reaction yields for a data set of amide-coupled C2-carboxylated 1,3-azoles. To facilitate molecular design, we integrated an interpretable heat-mapping algorithm named PIXIE (Predictive Insights and Xplainability for Informed chemical space Exploration). PIXIE visualizes the influence of molecular substructures on predicted yields by leveraging fingerprint bit importances, providing synthetic chemists with a powerful tool for the rational design of molecules. While heat mapping is an established technique, its integration with a machine-learning model tailored to the chemical space of C2-carboxylated 1,3-azoles represents a significant advancement. This approach not only enables targeted exploration of this underrepresented chemical space, fostering the discovery of new bioactive compounds, but also demonstrates the potential of combining these methods for broader applications in other chemical domains.