Guidelines for Machine Learning Yield Prediction from Small-Size Literature Dataset

18 May 2022, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


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 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.


Machine Learning
Reaction Yield Prediction

Supplementary materials

Supplementary Informations
Details on the code and the methods used to train the model and featurize the data. Additional information supporting the main manuscript.

Supplementary weblinks


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