Theoretical and Computational Chemistry

Computational evolution of new catalysts for the Morita–Baylis–Hillman reaction



We present a de novo discovery of an efficient catalyst of the Morita–Baylis–Hillman (MBH) reaction by searching chemical space for molecules that lower the estimated barrier of the rate determining step using a genetic algorithm (GA) starting from randomly selected tertiary amines. We performed five independent GA searches that resulted in 448 unique molecules, for which we were able to locate 435 true transitions states at semiempirical level of theory. The predicted activation energies of all 435 molecules where all lower than that of DABCO, which is a popular catalyst of the MBH reaction. Virtually all the molecules contain an aziridine N as the catalytically active site, which is discovered by the GA since it is either not found in the initial population or discarded early only to be redisovered as the search progresses. Many of the GA searches also introduce a substituent with a hydrogen bond donor that helps to stabilize the transition state and thus lower the barrier. Two molecules are selected for further study based on their synthetic accessibility as predicted by the retrosynthesis package Manifold. For these two molecules we compute the entire free energy reaction profile at the DFT level and show that their rate determining barriers are 1.7 and 2.4 kcal/mol lower than that of DABCO. The molecule with the lowest barrier has higher barriers for the other steps compared to DABCO, but none of the barriers are competitive with the rate-determining barrier and is predicted to outperform DABCO. This demonstrates the power of free exploration of chemical a space compared to more constrained fragment-based approaches.


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