The use of machine learning methods for the prediction of reaction yield is an emerging area.
We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields,
using combinatorial data. Molecular descriptors used in regression tasks related to chemical reac?tivity have often been based on time-consuming, computationally demanding quantum chemical
calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints
and molecular graphs) are quicker and easier to calculate, and are applicable to any molecule.
In this study, SVR models built on structure-based descriptors were compared to models built on
quantum chemical descriptors. The models were evaluated along the dimension of each reaction
component in a set of Buchwald-Hartwig amination reactions. The structure-based SVR models
out-performed the quantum chemical SVR models, along the dimension of each reaction compo?nent. The applicability of the models was assessed with respect to similarity to training. Prospec?tive predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to
validate the generalisability of the models, with particular interest along the aryl halide dimension.
Kernel Methods for Predicting Yields of Chemical Reactions
