![Author ORCID: We display the ORCID iD icon alongside authors names on our website to acknowledge that the ORCiD has been authenticated when entered by the user. To view the users ORCiD record click the icon. [opens in a new tab]](https://chemrxiv.org/engage/assets/public/chemrxiv/images/logos/orcid.png)
Abstract
A machine learning-based tool that provides conditions and predicted yields for Buchwald-Hartwig couplings from a ChemDraw™ structure input is described. The tool is built on an in-house generated experimental dataset that explores a diverse network of reactant pairings. To minimize the number of experiments necessary to produce models and maximize data value, a workflow based on unsupervised machine leaning tools was created. The workflow enables the construction of models which can successfully generalize—making predictions for reactants which are not represented in the dataset.
Supplementary materials
Title
Supplementary Materials
Description
Full experimental procedures including validation runs, characterization data, experimental apparatus, qHPLC analytical methodology, and copies of 1H, 13C, 31P, and 19F spectra can be found in the Supplementary Materials as well as feature engineering, modeling details and model validation, structures of each product made in the dataset, predictions for each condition with every reactant pair in the dataset.
Actions
