Abstract

The Programmable Cell Death Protein 1/Programmable Death-Ligand 1 (PD-1/PD-L1) interaction is an immune checkpoint utilized by cancer cells to enhance immune suppression. There exists a huge need to develop small molecules drugs that are fast acting, cheap, and readily bioavailable compared to antibodies. Unfortunately, synthesizing and validating large libraries of small-molecule to inhibit PD-1/PD-L1 interaction in a blind manner is a both time-consuming and expensive. To improve this drug discovery pipeline, we have developed a machine learning methodology trained on patent data to identify, synthesize and validate PD-1/PD-L1 small molecule inhibitors. Our model incorporates two features: docking scores to represent the energy of binding (E) as a global feature and sub-graph features through a graph neural network (GNN) to represent local features. This Energy-Graph Neural Network (EGNN) model outperforms traditional machine learning methods as well as a simple GNN with an average F1 score of 0.997 (± 0.004) suggesting that the topology of the small molecule, the structural interaction in the binding pocket, and chemical diversity of the training data are all important considerations for enhancing model performance. A Bootstrapped EGNN model was used to select compounds for synthesis and experimental validation with predicted high and low potency to inhibit PD-1/PD-L1 interaction. The new potent inhibitor, (4-((3-(2,3-dihydrobenzo[b][1,4]dioxin-6-yl)-2-methylbenzyl)oxy)-2,6-dimethoxybenzyl)-D-serine, is a hybrid of two known bioactive scaffolds, and has an IC50 values of 339.9 nM that is comparatively better than the known bioactive compound. We conclude that our EGNN model can identify active molecules designed by scaffold hopping, a well-known medicinal chemistry technique and will be useful to identify new potent small molecule inhibitors for specific targets.