Due to the recent successes of generative models much attention has been paid to de novo generation of drug-like molecules using machine learning. A particular class of generative models, diffusion probabilistic models, have recently been shown to work extraordinarily well across a diverse set of generative tasks, and a growing body of literature has applied diffusion probabilistic models directly to the molecule discovery problem. However, existing methods work with atom- based molecule representations, whereas work in the fragment-based drug design community indicates that using a molecular fragment-based approach can provide a much better inductive bias for the generative model. To this end, in our work we attempt to use diffusion probabilistic models to de novo generate drug-like molecules with a fragment-based representation, yielding more valid and drug-like molecules than existing approaches.