Choice of Machine Learning Model. Given the sparsity of data available for training a machine learning model, traditional architectures known to perform well with small amounts of data were evaluated. These machine learning architectures include regularized logistic regression, decision tree models, partial least squares, generalized linear models, and k-Nearest Neighbor. Each of these models solve classification problems in a very different manner. For example, logistic regression attempts to assign numeric weights to an input vector. This vector is then used to linearly transform the input into two probabilities for assignment to a given class. On the other hand, decision trees (when trained for classification) attempt to reduce the Shannon Entropy of the predicted class by splitting the data using a set of Boolean operations. This yields a flowchart of logical decisions that one can use to evaluate the decisions taken by the model (see the methods section for details of this procedure). The major advantage of decision tree models, with analytes represented as an input bit vector of functional groups, is that the resulting flow chart diagram can be interpreted by chemists to gain a deeper understanding of the chemistry resulting in a reaction taking place. This procedure is widely used in both biology and chemistry to identify and interpret how input features (in this case the collection of functional groups) correlate with a property of interest (reactivity with MOP in this case). Recently, similar techniques have been applied to reaction chemistry to understand how various chemical moieties are related to the reactivity of a molecule. Here, we used bootstrapping of several decision tree models to ensure robustness of our model for prospective experimental validations. Moreover, a comparison of the performance of decision trees to other machine learning models was also performed to ensure that efficacy was not compromised for the sole sake of interpretability.