Hitting rewind to predict multi-step chemical reactions

Date:
April 25, 2022
Source:
Hokkaido University
Summary:
Researchers overcome computational limitations to predict the
starting materials of multi-step reactions using only information
about the target product molecule.



FULL STORY ========================================================================== Researchers overcome computational limitations to predict the starting materials of multi-step reactions using only information about the target product molecule.


==========================================================================
Have you ever only caught the end of a TV show and wondered how the
story progressed to that ending? In a similar way, chemists often have a desired molecule in mind and wonder what kind of reaction could produce
it. Researchers in the Maeda Group at the Institute for Chemical Reaction Design and Discovery (ICReDD) and Hokkaido University developed a method
that can predict the "story" (i.e., the starting materials and reaction
paths) of multi-step chemical reactions using only information about the "ending" (i.e., the product molecules).

Predicting the recipe for a target product molecule, with no other
knowledge than the molecule itself, would be a powerful tool for
accelerating the discovery of new reactions. The Maeda group previously developed a computational method that succeeded in predicting single step reactions in this way. However, expanding to multi-step reactions leads to
a dramatic increase in the number of possible reaction pathways -- what
is known as combinatorial explosion. This sharp increase in complexity
results in prohibitively high calculation costs.

To overcome this limitation, researchers developed an algorithm that
reduces the number of paths that need to be explored by discarding less
viable paths at each step in the reaction. After calculating all possible
paths for one step backward in the reaction, a kinetic analysis method evaluates how well each path produces the target molecule. Reaction paths
that do not yield the target molecule above a pre-set threshold percentage
are deemed not significant enough, and are not explored further.

This cycle of exploring, evaluating, and discarding reaction paths is
repeated for each step backward in a multi-step reaction and mitigates
the combinatorial explosion that would normally occur, making multi-step reactions more feasible to calculate. Previous methods were limited
to single step reactions, whereas this new method was able to predict
reactions that involved more than 6 steps, marking a major jump in
capability.

As a proof-of-concept test, researchers tested the method on
two well-known multi-step reactions, the Strecker and Passerini
reactions. Thousands of starting material candidates were proposed for
each reaction, which were filtered to the most promising candidates
based on stability and product yield.

Critically, among the proposed candidates were the well-known starting materials for each reaction, confirming the ability of the technique to identify experimentally viable starting materials from just the target
product molecule.

Although further work is required to enable predicting even larger and
more complex systems, researchers anticipate that this breakthrough in
handling multi-step processes will accelerate the discovery of novel
chemical reactions.

"This work provides a unique approach, as it is the first time performing reverse predictions of multi-step reactions using quantum chemical
computations is possible without using any knowledge or data about the reaction," said Professor Satoshi Maeda. "We expect this technique will
enable the discovery of entirely unimagined chemical transformations,
in which case there is little knowledge or experimental data to use."

========================================================================== Story Source: Materials provided by Hokkaido_University. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Yosuke Sumiya, Yu Harabuchi, Yuuya Nagata, Satoshi Maeda. Quantum
Chemical Calculations to Trace Back Reaction Paths for the
Prediction of Reactants. JACS Au, 2022; DOI: 10.1021/jacsau.2c00157 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/04/220425104850.htm

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