EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions
Name
d3sc02048g.pdf
Description
Published version
Size
1.27 MB
Format
Adobe PDF
Checksum (MD5)
afd735c554eaae7f80aab7b5bd0f64a3
Author(s)
Heid, Esther
Probst, Daniel
Green, William H
Madsen, Georg KH
Date Issued
November 22, 2023
Journal
Chemical Science
Publisher
Royal Society of Chemistry
Citation
Heid, Esther, Probst, Daniel, Green, William H and Madsen, Georg KH. 2023. "EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions." Chemical Science, 14 (48).
Version
Final published version
Abstract
Enzymatic reactions are an ecofriendly, selective, and versatile addition, sometimes even alternative to organic reactions for the synthesis of chemical compounds such as pharmaceuticals or fine chemicals. To identify suitable reactions, computational models to predict the activity of enzymes on non-native substrates, to perform retrosynthetic pathway searches, or to predict the outcomes of reactions including regio- and stereoselectivity are becoming increasingly important. However, current approaches are substantially hindered by the limited amount of available data, especially if balanced and atom mapped reactions are needed and if the models feature machine learning components. We therefore constructed a high-quality dataset (EnzymeMap) by developing a large set of correction and validation algorithms for recorded reactions in the literature and showcase its significant positive impact on machine learning models of retrosynthesis, forward prediction, and regioselectivity prediction, outperforming previous approaches by a large margin. Our dataset allows for deep learning models of enzymatic reactions with unprecedented accuracy, and is freely available online.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1039/d3sc02048g
