Machine Learning‐Guided Computational Screening of New Candidate Reactions with High Bioorthogonal Click Potential
Name
Chemistry A European J - 2023 - Stuyver - Machine Learning‐Guided Computational Screening of New Candidate Reactions with.pdf
Description
Published version
Size
4.23 MB
Format
Adobe PDF
Checksum (MD5)
3bdb3fb8596c3f19f4860838b7708755
Author(s)
Stuyver, Thijs
Coley, Connor W
Date Issued
May 16, 2023
Journal
Chemistry – A European Journal
Publisher
Wiley
Citation
T. Stuyver, C. W. Coley, Chem. Eur. J. 2023, 29, e202300387.
Version
Final published version
Abstract
Bioorthogonal click chemistry has become an indispensable part of the biochemist's toolbox. Despite the wide variety of applications that have been developed in recent years, only a limited number of bioorthogonal click reactions have been discovered so far, most of them based on (substituted) azides. In this work, we present a computational workflow to discover new candidate reactions with promising kinetic and thermodynamic properties for bioorthogonal click applications. Sampling only around 0.05 % of an overall search space of over 10,000,000 dipolar cycloadditions, we develop a machine learning model able to predict DFT‐computed activation and reaction energies within ∼2–3 kcal/mol across the entire space. Applying this model to screen the full search space through iterative rounds of learning, we identify a broad pool of candidate reactions with rich structural diversity, which can be used as a starting point or source of inspiration for future experimental development of both azide‐based and non‐azide‐based bioorthogonal click reactions.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1002/chem.202300387
