Affiliation:
1. Department of Chemical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge Massachusetts 02139 US
2. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge Massachusetts 02139 US
Abstract
AbstractBioorthogonal 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.
Subject
General Chemistry,Catalysis,Organic Chemistry
Cited by
6 articles.
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