Next, we construct the initial VPs space for molecules oriented to closely align with the x-y plane. We present two efficient sampling strategies. For the coarse-grained sampling strategy, this approach involves sampling VPs within a broadly predefined space, without any prior molecular structure information. During training, the number of VPs is dynamically adjusted according to the total number of atoms in the molecule, falling within specified lower and upper ratio bounds. This adjustment allows the VPs to adapt to the size of the molecule. A cubic space is constructed using atomic coordinates, within which VPs are uniformly generated. The VPs are then continuously shifted by the generator to model the actual atomic distribution. Furthermore, to reduce resource consumption, the initial space for VPs can be defined based on the smallest volume constructed by projecting the molecular atomic coordinates on the x, y, and z axes (this can be implemented via PCA ). During inference, this predefined space distribution from the training set is used to initialize space for generation. For the fine-grained sampling strategy, considering the need for efficient generation of large, complex OOMs that fit real-world scenarios, we employ prior spatial structural information for effective generation and structural refinement. Specifically, we construct resource-optimization spatial grids based on the spatial structure information of template molecules and initialize VPs within these grids around each atom to better simulate the complex atomic distribution of such OOMs. This approach enables faster iterative generation of the desired structural OOMs.