Molecular Machine Learning for Asymmetric (Organo-)Catalysis

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

Asymmetric catalysis plays a pivotal role in the synthesis of pharmaceutically active compounds. Organocatalysis aims to enable such transformations without the use of potentially toxic metals. Its biggest challenge, however, lies in the design of potent organocatalysts and prediction of activity: In most cases, the correlation between a catalyst's molecular structure and its activity is poorly understood. Thus, we will develop and apply machine learning (ML) techniques to thiourea and oligopeptide catalyst libraries: We aim to determine the most viable candidates for organocatalytic transformations of pharmaceutical interest, such as for the synthesis of anti-malarial agents. By using explainable AI techniques on our ML models, we plan to reveal the molecular features responsible for catalyst activity to move further towards the goal of true de novo catalyst design.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2363: Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning
• Co-Investigator: Dr. Dennis Gerbig