Merging enzymatic and synthetic chemistry with computational synthesis planning
Name
s41467-022-35422-y.pdf
Description
Published version
Size
1.4 MB
Format
Adobe PDF
Checksum (MD5)
befb946c5f6da5f619b55dbf97b01ab2
Author(s)
Levin, Itai
Liu, Mengjie
Voigt, Christopher A
Coley, Connor W
Date Issued
December 14, 2022
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Levin, Itai, Liu, Mengjie, Voigt, Christopher A and Coley, Connor W. 2022. "Merging enzymatic and synthetic chemistry with computational synthesis planning." Nature Communications, 13 (1).
Version
Final published version
Abstract
AbstractSynthesis planning programs trained on chemical reaction data can design efficient routes to new molecules of interest, but are limited in their ability to leverage rare chemical transformations. This challenge is acute for enzymatic reactions, which are valuable due to their selectivity and sustainability but are few in number. We report a retrosynthetic search algorithm using two neural network models for retrosynthesis–one covering 7984 enzymatic transformations and one 163,723 synthetic transformations–that balances the exploration of enzymatic and synthetic reactions to identify hybrid synthesis plans. This approach extends the space of retrosynthetic moves by thousands of uniquely enzymatic one-step transformations, discovers routes to molecules for which synthetic or enzymatic searches find none, and designs shorter routes for others. Application to (-)-Δ9 tetrahydrocannabinol (THC) (dronabinol) and R,R-formoterol (arformoterol) illustrates how our strategy facilitates the replacement of metal catalysis, high step counts, or costly enantiomeric resolution with more elegant hybrid proposals.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41467-022-35422-y
