Prediction of the Chemical Context for Buchwald-Hartwig Coupling Reactions
Journal article, 2022

We present machine learning models for predicting the chemical context for Buchwald-Hartwig coupling reactions, i. e., what chemicals to add to the reactants to give a productive reaction. Using reaction data from in-house electronic lab notebooks, we train two models: one based on single-label data and one based on multi-label data. Both models show excellent top-3 accuracy of approximately 90 %, which suggests strong predictivity. Furthermore, there seems to be an advantage of including multi-label data because the multi-label model shows higher accuracy and better sensitivity for the individual contexts than the single-label model. Although the models are performant, we also show that such models need to be re-trained periodically as there is a strong temporal characteristic to the usage of different contexts. Therefore, a model trained on historical data will decrease in usefulness with time as newer and better contexts emerge and replace older ones. We hypothesize that such significant transitions in the context-usage will likely affect any model predicting chemical contexts trained on historical data. Consequently, training context prediction models warrants careful planning of what data is used for training and how often the model needs to be re-trained.


context prediction

condition prediction

Buchwald-Hartwig coupling reactions


Samuel Genheden

AstraZeneca AB

Agnes Mårdh

Student at Chalmers

AstraZeneca AB

Gustav Lahti

AstraZeneca AB

Student at Chalmers

Ola Engkvist

AstraZeneca AB

Chalmers, Computer Science and Engineering (Chalmers)

Simon Olsson

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Thierry Kogej

AstraZeneca AB

Molecular Informatics

1868-1743 (ISSN) 1868-1751 (eISSN)

Vol. In Press

Subject Categories

Other Computer and Information Science

Subatomic Physics

Probability Theory and Statistics





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3/8/2022 2