Predicting three-component reaction outcomes from ~40,000 miniaturized reactant combinations
Year: 2025
Type: article
Abstract: Efficient drug discovery depends on reliable synthetic access to candidate molecules, but emerging machine learning approaches to predicting reaction outcomes are hampered by poor availability of high-quality data. Here, we demonstrate an on-demand synthesis platform based on a three-component reaction that delivers drug-like molecules. Miniaturization and automation enable the execution and analysis of 50,000 distinct reactions on a 3-microliter scale from 193 different substrates, producing the largest public reaction outcome dataset. With machine learning, we accurately predict the result of unknown reactions and analyze the impact of dataset size on model training, both enabling accurate outcome predictions even for unseen reactants and providing a sufficiently large dataset to critically evaluate emerging machine learning approaches to chemical reactivity.
Source: Science Advances
Authors: Julian Götz, Euan Richards, Iain A. Stepek, Yu Takahashi, Yi-Lin Huang, Louis Bertschi, Bertran Rubi, Jeffrey W. Bode
Institutions: ETH Zurich, Laboratory of Organic Chemistry
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,500
OpenAlex ID: W4410809633