Generative models for molecules have shown considerable promise for use in computational chemistry, but remain difficult to use for non-experts. For this reason, we introduce open-source infrastructure for easily building generative molecular models into the widely used DeepChem library with the aim of creating a robust and reusable molecular generation pipeline. In particular, we add high quality PyTorch implementations of the Molecular Generative Adversarial Networks (MolGAN) and Normalizing Flows. Our implementations show strong performance comparable with past work.