Despite our extensive evaluation of this approach, there are several opportunities for future work. The most obvious area for improvement of the PGN package would be including more diverse graph convolutional methods, such as Deep Tensor Networks and other related architectures that represent distance and angle information more natively. Additionally, exploring different data augmentation techniques to improve model performance in low-data situations would be advantageous. Beyond simple improvements to the PGN package, we also envision that PGN could facilitate analyses of molecular dynamics simulations and assist virtual screen approaches. Additionally, the strong generalization of our PGN models makes model fine-tuning for low-data situations another potential application that could aid drug discovery.