MoFlow is an invertible flow-based generative model for molecular graphs. It jointly learns a bijective mapping between molecules (atom types + bond types) and a latent Gaussian, enabling exact likelihood, 100% reconstruction, random generation, interpolation, and property-guided optimization of small molecules.