This study combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs) to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimental data from in-house HTE and literature, then used to predict the coupling of 3180 heterocyclic building blocks with various sp3-rich carboxylic acids. This led to the synthesis and characterization of 30 novel, functionally modified molecules.
Publisher
Communications Chemistry
Published On
Nov 20, 2023
Authors
David F. Nippa, Kenneth Atz, Alex T. Müller, Jens Wolfard, Clemens Isert, Martin Binder, Oliver Scheidegger, David B. Konrad, Uwe Grether, Rainer E. Martin, Gisbert Schneider
Tags
C-H alkylation
high-throughput experimentation
graph neural networks
minisci-type chemistry
novel molecules
heterocyclic building blocks
Related Publications
Explore these studies to deepen your understanding of the subject.
