De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates

Very recently our group has demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover a new and efficient catalyst for the alcohol-mediated Morita- Baylis-Hillman (MBH) reaction. In particular, the discovered catalyst was shown experimentally to be eight times more active than 1,4-diazabicyclo[2.2.2]octane (DABCO), which is commonly used to catalyze the MBH reaction. This represents a breakthrough in using generative models for catalyst optimization. However, the GA-procedure, and hence discovery, relied on two important pieces of information; 1) the knowledge that tertiary amines catalyze the reaction and 2) the mechanism and reaction profile for the catalyzed reaction, in particular the transition state structure of the rate determining step. Thus truly de novo catalyst discovery must also include these steps. Here we present such a method for discovering catalyst candidates for a specific reaction while simultaneously proposing a mechanism for the catalyzed reaction. We use the method to show that tertiary amines and phosphines are potential catalysts for the MBH reaction by screening a selection of 11 molecular templates representing common functional groups. The presented method relies on an automated reaction discovery workflow using meta-dynamics calculations. Combining this method for catalyst candidate discovery with our GA-based catalyst optimization method results in an algorithm for truly de novo catalyst discovery