Predicting three-component reaction outcomes from ~40,000 miniaturized reactant combinations
OPEN ACCESS
Author / Producer
Date
2025-05
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
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.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
11 (22)
Pages / Article No.
Publisher
AAAS
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
02892 - NEXUS Personalized Health / NEXUS Personalized Health