Cancer immunotherapy marks a major step in treating cancer and the development of PD-1/PD-L1 immune checkpoint inhibitors have been an important area of research for treatment of several tumors. Currently, six therapeutic antibodies targeting both PD-1 (pembrolizumab, nivolumab, and cemiplimab) and PD-L1 (atezolizumab, durvalumab, and avelumab) have been approved by U.S. FDA. Recently, several new small molecules PD-1/PD-L1 inhibitors have been developed along with structure determination of human PD-1/PD-L1 complex and co-crystals of inhibitory ligands . Still the field is very active in search for new small molecules to inhibit this important checkpoint and we hope to enhance the speed of this search with the use of new structure-based ML methods that have been benchmarked extensively and tested prospectively. Our EGNN methodology can be further developed with the addition of more chemically diverse data, and incorporating reinforcement iterative learning with experiments performed in each step for developing a library of structurally diverse small molecule inhibiting PD-1/PD-L1 interaction to guide structure-activity relationships. Given the general nature of the machine learning model and docking methodology that is readily available for use, this approach can be adapted to identify small molecule immunomodulators by targeting other immune checkpoints, as well as, generally used to include local and global features for target-based drug design.