Machine Learning‐Guided Computational Screening of New Candidate Reactions with High Bioorthogonal Click Potential

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.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Machine Learning‐Guided Computational Screening of New Candidate Reactions with High Bioorthogonal Click Potential ; day:04 ; month:04 ; year:2023 ; extent:11 ; Chemistry - a European journal ; (04.04.2023) (gesamt 11)

Contributors, Places and Time

• Creator: Stuyver, Thijs ; Coley, Connor W.

Further information

• DOI: 10.1002/chem.202300387
• URN: urn:nbn:de:101:1-2023040415284953286649
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:58 AM CEST