In this paper, we propose a tripartite deep graph generative model, MoFlowGAN, for targeted molecular graph generation. MoFlowGAN consists of a normalizing flow generator that learns invertible mappings for bond networks and atoms, a GCN-based discriminator which allows for adversarial training, and a modular reward network that can be used to heuristically bias the generator towards "improved outputs". MoFlowGAN is competitive with recent state-of-the-art models including its parent architectures. Future work will explore MoFlowGAN's performance on larger datasets with more druglike molecules, such as ZINC250 , the Pareto objective front, as well as the tradeoff of between learning objectives and model size.