Predicting success in Cu-catalyzed C-N coupling reactions using data science.

Area of Science:

• Synthetic Chemistry
• Computational Chemistry
• Data Science

Background:

• Data science and computational chemistry are crucial for optimizing synthetic reactions and managing experimental workloads.
• Integrating these tools with high-throughput experimentation maximizes success in costly synthetic campaigns.
• Predicting reaction outcomes is essential for efficient chemical synthesis.

Purpose of the Study:

• To develop an end-to-end data-driven process for predicting the impact of structural features on Cu-catalyzed C-N coupling reactions.
• To identify limitations of substrates and ligands in these reactions.
• To create a systematic ligand prediction tool using probabilistic assessment.

Main Methods:

• Utilized a data-driven workflow integrating computational chemistry and data science.
• Analyzed structural features of coupling partners and ligands.
• Developed a probabilistic model for ligand success prediction.

Main Results:

• Successfully predicted the influence of structural features on Cu-catalyzed C-N coupling reactions.
• Identified key limitations associated with specific substrates and ligands.
• Demonstrated a systematic approach to ligand selection based on probability.

Conclusions:

• The data-driven platform effectively predicts ligand performance in Cu-catalyzed C-N coupling.
• This approach addresses the inherent unpredictability in synthetic reaction deployment.
• The developed tool aids in maximizing success rates for synthetic campaigns.