Kernel Methods for Predicting Yields of Chemical Reactions

Abstract

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 reactivity 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 structurebased 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 outperformed the quantum chemical SVR models, along the dimension of each reaction component. The applicability of the models was assessed with respect to similarity to training. Prospective predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalizability of the models, with particular interest along the aryl halide dimension.

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“…The radius determines the number of iterations in the calculation of the identifier of the central atom. With the increase of the radius, the information of the surrounding substructure is increasingly encoded into the identifier [38,39]. Each identifier is updated iteratively to include information on neighbouring atoms (i.e., their identifier and bond order).…”

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“…[1][2][3][4][5][6] Similarly, developments in machine learning (ML) have enabled the distillation of large and complex data sets into predictive models capable of generalizing patterns in the data. 4,[7][8][9][10][11][12][13] Despite these advances, efforts to merge HTE with ML remains largely limited to a few reported datasets with limited structural diversity [14][15][16][17][18][19][20] and corresponding trained models that do not extrapolate well to substrates beyond the training set.…”

“…Modeling the yield of these datasets (4K C-N couplings 15 , or 2K Suzuki-Miyaura couplings in flow 14 ) produces predictive models with R 2 or AUROC > 0.9. 11,15,[27][28][29][30][31][32][33][34] However, models trained on these datasets demonstrate limited ability to extrapolate beyond the molecules in their training sets, in part due to the minimal structural diversity in the dataset.…”

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