Machine Learning for Organocatalysis in the Small Data Regime

• Applicant: Professor Dr. Peter R. Schreiner
• Subject Area: Organic Molecular Chemistry - Synthesis and Characterisation
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 497198316

The proposal focuses on advancing machine learning (ML) applications in organocatalysis using curated, high-quality small experimental datasets. Key goals include refining enantioselective catalyst design, especially for challenging reactions like the Corey-Bakshi-Shibata (CBS) reduction and the Dakin-West reaction, using a "key-intermediate-graph" approach. The project will further extend ML applications to ketimine reductions and meso-anhydride desymmetrization, addressing limitations in the current state-of-the-art. We plan on creating a user-interactive ML system for real-time predictions and enhancing dataset quality through modular peptide design. Our efforts also include (inter)national collaborations and making our ML platform available to other groups via bench-top implementations.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2363: Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning