Recently, various diffusion models have been used for 3D molecule generation in both LBDD and SBDD scenario, utilizing an iterative denoising process. Welling et al. proposed the equivariant diffusion model (EDM) for 3D molecule generation, improving significantly over previous results in one-shot or auto-regressive settings. The MDM and GCDM 21 modified EDM by limiting the message-passing computations to neighboring nodes and changing a more expressive denoising model. Xu et al. proposed GeoLDM, a diffusion model on latent space coded by an equivariant autoencoder instead of feature space as defined in EDM. Although these methods have shown promising results, they ignored the connectivity in molecular generations, leading to sub-optimal performance on complex molecules. To overcome this issue, Frossard et al. proposed a mixed graph and 3D denoising diffusion model, MIDI , by simultaneously generating a molecular graph and its corresponding 3D coordinates. So far, these diffusion models are conditioned in two ways: conditional guidance existed in the sampling process not in the training stage or using direct control during the training stage with specific model architectures. In former case, an additional end-to-end differential scoring function or model is needed to guide the pre-trained generative model to sample molecules with targeted properties, for example, the KGDiff model by Xu et al. and SLIVER model by Mey et al. In later case, models such as DiffSBDD 25 , DiffBP 11 , and TargetDiff 26 were used to generate molecules that bind to a specific protein pocket, the DiffLinker 27 was proposed to generate linkers between molecular fragments. Although these methods improved the conditioned generation in various tasks, all of them need to be trained from scratch for each task which may lead to high computation cost.