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

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Chemical Synthesis

Background:

• Carbon dioxide (CO2) conversion into valuable chemical building blocks like C2-carboxylated 1,3-azoles is crucial for pharmaceuticals, cosmetics, and pesticides.
• A limited number of available 1,3-azoles are carboxylated at the C2 position, indicating a need for improved synthetic strategies and exploration of this chemical space.

Purpose of the Study:

• To develop a supervised machine learning model for predicting reaction yields of amide-coupled C2-carboxylated 1,3-azoles.
• To integrate an interpretable heat-mapping algorithm (PIXIE) for visualizing molecular substructure influence on predicted yields, facilitating rational molecular design.

Main Methods:

• Utilized a supervised machine learning approach to analyze a dataset of amide-coupled C2-carboxylated 1,3-azoles.
• Employed the PIXIE algorithm, which uses fingerprint bit importances to generate heat maps, illustrating the impact of molecular features on reaction outcomes.

Main Results:

• The study successfully developed a predictive model for reaction yields in this specific chemical class.
• PIXIE provided interpretable visualizations, highlighting key molecular substructures that influence predicted yields, thereby aiding synthetic chemists.

Conclusions:

• The integration of machine learning and interpretable heat mapping offers a powerful approach for exploring underrepresented chemical spaces like C2-carboxylated 1,3-azoles.
• This methodology facilitates the targeted discovery of novel bioactive compounds and demonstrates potential for broader applications in chemical research and development.