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

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.