Issue 22, 2022

Predicting reaction conditions from limited data through active transfer learning

Abstract

Transfer and active learning have the potential to accelerate the development of new chemical reactions, using prior data and new experiments to inform models that adapt to the target area of interest. This article shows how specifically tuned machine learning models, based on random forest classifiers, can expand the applicability of Pd-catalyzed cross-coupling reactions to types of nucleophiles unknown to the model. First, model transfer is shown to be effective when reaction mechanisms and substrates are closely related, even when models are trained on relatively small numbers of data points. Then, a model simplification scheme is tested and found to provide comparative predictivity on reactions of new nucleophiles that include unseen reagent combinations. Lastly, for a challenging target where model transfer only provides a modest benefit over random selection, an active transfer learning strategy is introduced to improve model predictions. Simple models, composed of a small number of decision trees with limited depths, are crucial for securing generalizability, interpretability, and performance of active transfer learning.

Graphical abstract: Predicting reaction conditions from limited data through active transfer learning

Supplementary files

Article information

Article type
Edge Article
Submitted
10 12月 2021
Accepted
10 5月 2022
First published
11 5月 2022
This article is Open Access

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

Chem. Sci., 2022,13, 6655-6668

Predicting reaction conditions from limited data through active transfer learning

E. Shim, J. A. Kammeraad, Z. Xu, A. Tewari, T. Cernak and P. M. Zimmerman, Chem. Sci., 2022, 13, 6655 DOI: 10.1039/D1SC06932B

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