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Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings

Abstract : Synthetic yield prediction using machine learning is intensively studied. Previous work focused on two categories of datasets: High-Throughput Experimentation data, as an ideal case study and datasets extracted from proprietary databases, which are known to have a strong reporting bias towards high yields. However, predicting yields using published reaction data remains elusive. To fill the gap, we built a dataset on 1 nickel-catalyzed cross-couplings extracted from organic reaction publications, including scope and optimization information. We demonstrate the importance of including optimization data as a source of failed experiments and emphasize how publication constraints shape the exploration of the chemical space by the synthetic community. While machine learning models still fail to perform out-of-sample predictions, this work shows that adding chemical knowledge enables fair predictions in a low-data regime. Eventually, we hope that this unique public database will foster further improvements of machine learning methods for reaction yield prediction in a more realistic context.
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https://hal.sorbonne-universite.fr/hal-03790865
Contributor : Jules Jules SCHLEINITZ Connect in order to contact the contributor
Submitted on : Wednesday, September 28, 2022 - 5:08:50 PM
Last modification on : Wednesday, November 9, 2022 - 4:42:11 PM

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Jules Schleinitz, Maxime Langevin, Yanis Smail, Benjamin Wehnert, Laurence Grimaud, et al.. Machine Learning Yield Prediction from NiCOlit, a Small-Size Literature Data Set of Nickel Catalyzed C–O Couplings. Journal of the American Chemical Society, 2022, 144 (32), pp.14722-14730. ⟨10.1021/jacs.2c05302⟩. ⟨hal-03790865⟩

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