However, existing deep generative models encounter significant difficulties when dealing with OOMs. Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) , which utilize sequential or graph-based molecular representations, face challenges in generating accurate three-dimensional (3D) molecular conformations, which are important for the optoelectronic properties of OOMs. Recent endeavors integrating 3D-equivariant networks with diffusion have demonstrated promising outcomes in small molecule generation. Nevertheless, our experiments reveal that these methods exhibit slow convergence and low validity when applied to fused-ring molecules. Geometric Bayesian Flow models , despite harnessing 3D structural information and diminishing input noise to achieve faster convergence for smaller molecules, also yield low validity results for OOMs. As a pivotal development, Tomer Weiss et al. proposed a method of mitigating complexity by using the graph of rings (GOR) representation. This approach significantly boosts the efficiency and validity of generating fused-ring molecules. However, this method restricts, to some extent, the chemical space to be searched and the novelty of the generated molecules .