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 targ

SUBMITTER: Shim E 

PROVIDER: S-EPMC9172577 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature