Issue 19, 2023

A focus on the use of real-world datasets for yield prediction

Abstract

The prediction of reaction yields remains a challenging task for machine learning (ML), given the vast search spaces and absence of robust training data. Wiest, Chawla et al. (https://doi.org/10.1039/D2SC06041H) show that a deep learning algorithm performs well on high-throughput experimentation data but surprisingly poorly on real-world, historical data from a pharmaceutical company. The result suggests that there is considerable room for improvement when coupling ML to electronic laboratory notebook data.

Graphical abstract: A focus on the use of real-world datasets for yield prediction

Article information

Article type
Commentary
First published
27 apr 2023
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2023,14, 4958-4960

A focus on the use of real-world datasets for yield prediction

L. Bustillo and T. Rodrigues, Chem. Sci., 2023, 14, 4958 DOI: 10.1039/D3SC90069J

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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