Catalyst Evolution: Study of Change

Using a genetic algorithm to uncover new catalyst molecules for organic synthesis

A team of researchers have used a computational method inspired by
evolution to discover an organic catalyst with better performance than
known catalysts. As the team reports in the journal Angewandte Chemie,
a genetic algorithm suggested new, catalytically active molecular
structures for a popular reaction in organic synthesis. The method could
be applied more broadly to the search for better molecular catalysts,
the team says.

The Evolution of a Catalyst - Using a genetic algorithm to uncover new catalyst molecules for organic synthesis

© Wiley-VCH, re-use with credit to ‘Angewandte Chemie’ and a link to the original article.

Machine learning systems can already predict material properties and
molecular structures in a variety of fields of chemistry with high
precision. However, automating the search for new and improved catalysts
has to date not been possible, although developing new catalysts for
chemical reactions is currently one of the most important goals in
chemical research. More efficient catalysts open the door to quicker and
easier reactions that consume less energy and form fewer by-products.

The reason behind the difficulties encountered by automated systems
when seeking new catalysts lies in reaction transition states, as Jan
Halborg Jensen, Professor of computational chemistry at the University
of Copenhagen, Denmark, and corresponding author for the study,
explains. This is because catalysts influence the transition state; in
other words, the moment in a reaction that decides whether a product
will be formed or not. The fleeting nature of this moment, and the
complexity of the structures formed, with many molecules interacting at
the same time, make it difficult to develop models.

To overcome this, Jensen and the team turned to a selection method
based on the principles of evolution. A genetic algorithm was used to
evaluate a set of starting molecules for fitness for catalyzing the
Morita-Baylis-Hillman (MBH) reaction. “Then you take the fittest
molecules and mate them, which means that you cut the two parents in
random places and recombine fragments from each parent,” Jensen
explains. “If you do this enough times, the final population can look
very different from the initial population, much like a chihuahua
differs from its wolf ancestors.”

In this way, the final molecules generated by the computer had a new
structural motif, a four-membered azetidine ring, which was not present
in the initial population. The team then synthesized one of the
computer-evolved azetidine candidates and tested it in the reaction,
finding that it performed considerably better than the traditional
catalyst, DABCO (1,4-diazabicyclo[2.2.2]octane). “Azetidines had never
been considered as catalysts for the MBH reaction, so the algorithm made
a genuinely novel discovery,” says Jensen, highlighting the importance
of computer-assisted discoveries in chemical research.

Jensen says that an essential prerequisite for the use of this
technique in the future is the knowledge of the key transition state for
the reaction in question. He believes that if this is known, genetic
algorithms could help to identify new and improved organocatalysts.

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About the Author

Prof. Jan Halborg
heads a computational quantum chemistry group at the Department
of Chemistry of the University of Copenhagen, Denmark. Working on the
discovery of new molecules and chemical reactions at the interface of
machine learning and quantum chemistry, the Jensen group has revitalized
the use of genetic algorithms in molecule discovery and shown that fast
quantum mechanical methods can be used in high-throughput screening of
chemical reactivity.

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