TY - JOUR

T1 - Toward De Novo Catalyst Discovery

T2 - Fast Identification of New Catalyst Candidates for Alcohol-Mediated Morita–Baylis–Hillman Reactions**

AU - Rasmussen, Maria H.

AU - Seumer, Julius

AU - Jensen, Jan H.

N1 - Funding Information: This work was supported by Novo Nordisk Fonden via grant number NNF20OC0064104. Publisher Copyright: © 2023 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.

PY - 2023

Y1 - 2023

N2 - Recently we have 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 DABCO, 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 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 show that tertiary amines and phosphines are potential catalysts for the MBH reaction by screening 11 molecular templates representing common functional groups. The 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.

AB - Recently we have 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 DABCO, 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 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 show that tertiary amines and phosphines are potential catalysts for the MBH reaction by screening 11 molecular templates representing common functional groups. The 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.

KW - catalysis

KW - de novo reaction discovery

KW - organocatalysis

U2 - 10.1002/anie.202310580

DO - 10.1002/anie.202310580

M3 - Journal article

C2 - 37830522

AN - SCOPUS:85175147001

VL - 62

JO - Angewandte Chemie International Edition

JF - Angewandte Chemie International Edition

SN - 1433-7851

IS - 49

M1 - e202310580

ER -