Graph generation is a critical yet challenging task as empirical analyses require a deep understanding of complex, non-Euclidean structures. Although diffusion models have recently made significant achievements in graph generation, these models typically adapt from the frameworks designed for image generation, making them ill-suited for capturing the topological properties of graphs.In this work, we propose a Cellular-Guided Graph Generative (CG3) model that follows a coarse-to-fine generation curriculum and is guided by cellular information, enabling the progressive generation of authentic graphs with inherent topological structures.Experimental results show that our method is able to generate molecules that lie close to the training distribution yet do not violate the chemical valency rule, demonstrating the effectiveness of CG3 in modelling the higher-order relationships.