(iv) highly inefficient to identify several new scaffolds rapidly. In addition, virtual screening using docking methods have been developed to improve this process but with limited success. Further, ML architectures such as Support Vector Machine (SVM) , Random Forest (RF) , Graph Convolution Network , and Graph Neural Networks (GNN) have been used for drug design and predicting drug-target interactions . Recently, new architectures utilizing a combination of graph features in the binding site of a protein have shown great promise for calculating binding affinities and determining whether a compound will bind to a target. Several new neural network-based architectures have also been proposed which promise to identify potent scaffolds, but many have not been tested experimentally, and developments in the ability to mine and characterize protein crystallography data hopes to drive the creation of these models. Recently, it has been shown that molecular sub-graph features incorporated through a GNN and protein features encoded by their sequence can be combined to predict if a compound can target a given protein. Inspired by this work and based on our interest in developing methods for drug design and immunology , we have developed a new machine learning model to predict if a compound can inhibit the PD-1/PD-L1 interaction. Our method replaces the protein sequence features with docking scores representing the free energy of binding and due to this global energetic interaction of the small molecule in the binding pocket, we have termed this model as an "Energy Graph Neural Network" (EGNN). The three-dimensional atomic interaction energetic scores are calculated using CANDOCK (Figure ) and are combined with local molecular graph features (Figure ) using an end-to-end training methodology (Figure ). In this work, we use this EGNN model to select designs for synthesis, and experimentally test a curated list of compounds from these predictions to prospectively identify potent PD-L1 small molecule inhibitors using the Homogenous Time-Resolved Fluorescence (HTRF) assay. We also tested negative predictions suggesting the utility of the model to be used for selecting potent leads as PD-1/PD-L1 inhibitors.