Molecular Machine Learning for Asymmetric (Organo-)Catalysis

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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.