Predictive Modeling. Given the potential breath of PTDI SNAr as a method to access a wide diversity of pharmaceutically relevant products, we sought the use of machine learning to generate a predictive model (Scheme 4). We posited this was especially important when exploring complex reaction partners that could be challenging to intuit structure reactivity relationships. Since our data set was relatively small (~100 reactions) and used only one prototypical electrophile to explore the nucleophile scope (and one nucleophile to explore the electrophile scope), we leveraged high-throughput experimentation (HTE) to add data from 96 diverse reactions. This was designed to somewhat randomly sample the unexplored reaction space (see SI for visualization, Scheme S14). The main objective was to define a 'yes/no' classification model for product formation ('yes' defined as >5% conversion determined by LCMS) since we observed most reactions could be further optimized using solvent selection or temperature as long as some product was observed. To develop a predictive model that combines simplicity and interpretability, we focused on a decision tree classifier. To build a descriptor set that characterized the electronic and steric properties of the reaction partners, we conducted quantum-chemical calculations and extracted a range of molecular features (see details on molecular features in SI). To train the decision tree, we utilized a random stratified split of the dataset into 70/30 training/test. The best performing decision tree was comprised of three nodestwo nodes related to electrophile properties and one node related to nucleophile properties. The model achieves high accuracy in classifying product formation as 'yes' or 'no'. Notably, the test set accuracy reaches 96%, along with an average accuracy of 93% in a fivefold cross-validation on the complete dataset (Scheme 4, middle, see computational details and complete model metrics Scheme S14).