Programmed cell death protein 1 (PD-1) is an immune checkpoint receptor implicated for the creation of new cancer therapeutics. The prolonged interaction between the T-cell receptor and the major histocompatibility complex (MHC) leads to upregulation of PD-1 on the activated T-cell surface. Activated T cells produce cytokines, such as Interferon-γ, which in turn cause tumor cells to express programmed death ligand 1 (PD-L1) on the their cell surface. Tumors escape the action of immune system by utilizing the interaction between PD-1 and ligand PD-L1 resulting in lower effector T-cell function and survival, as such resulting in a suppressive immune response in the tumor microenvironment. The inhibition of the PD-1/PD-L1 interaction can enhance anti-tumor immunity and a large amount of work has been done to develop monoclonal antibodies as inhibitors of PD-1/PD-L1 interaction inhibitors. For example, pembrolizumab, cemiplimab, and nivolumab are three FDA approved anti-PD-1 monoclonal antibodies. The discovery of small-molecule inhibitors would be an advantageous over monoclonal antibodies, such as being fast-acting, simple for in vivo administration, ability to penetrate through cell membranes and interact with the cytoplasmic domains of cell surface receptors. Since a few years, there has been significant development in designing PD-1/PD-L1 inhibitors. Specifically, Bristol-Myers Squibb (BMS) discovered a set of potent PD-1/PD-L1 small molecule inhibitors based on the peptidomimetic molecules and non-peptidic small molecules. In particular, BMS revealed a 2-methyl-3-biphenyl-methanol scaffold containing chemical libraries. Later, Holak et al. studied the interaction of BMS molecules with PD-L1 suggesting that BMS molecules induce PD-L1 dimerization and also reported crystal structures of compounds with dimeric PD-L1. Based on these findings, we envisioned to develop a machine learning (ML) framework for selecting and testing new PD-1/PD-L1 inhibitors.