A Machine Learning Approach to Model Interaction Effects: Development and Application to Alcohol Deoxyfluorination

The application of machine learning (ML) techniques to model high-throughput experimentation (HTE) datasets has seen a recent rise in popularity. Nevertheless, the ability to model the interplay between reaction components, known as interaction effects, with ML remains an outstanding challenge. Using a simulated HTE dataset, we find that the presence of irrelevant features poses a strong obstacle to learning interaction effects with common ML algorithms. To address this problem, we propose a two-part statistical modeling approach for HTE datasets: classical analysis of variance of the experiment to identify systematic effects that impact reaction yield across the experiment followed by regression of individual effects using chemistry-informed features. To illustrate this methodology, we use our previously published alcohol deoxyfluorination dataset comprising 740 reactions to build a compact, interpretable generalized additive model that accounts for each significant effect observed in the dataset. We achieve a sizeable performance boost compared to our previously published random forest model, reducing mean absolute error from 18 to 13% and root-mean-squared error from 22 to 17% on a newly generated validation set. Finally, we demonstrate that this approach can facilitate the generation of new mechanistic hypotheses, which, when probed experimentally, can lead to a deeper understanding of chemical reactivity.