(A) General workflow for modeling chemical reactions: Dataset design initiated with a database query to create a library of commercially available substrates and ligands. These molecules are then parametrized using quantum-chemical calculations and clustered by similarity using dimensionality reduction and unsupervised ML techniques. Subsequently, molecules from each cluster are selected to form a diverse and representative substrate space for training the ML model. The training sets can be refined using active learning strategies that employ classification models to identify substrate and ligand features responsible for activity. These insights, in turn, guide the selection of new substrates and/or ligands. The reaction output resulting from the final combination of substrates and ligands are then employed to train a predictive ML model. (B) Synthetic applications of C-N cross couplings in drug development. Black and blue atoms and bonds represent moieties originally from the aryl-bromide and primary amine, respectively. Grey atoms and bonds indicate chemical transformations made after the C-N cross-coupling. (C) Reaction conditions utilized in this work and illustration of the tools utility, including confidence values on the prediction and top suggested ligands for testing.