Inspired by the well-known ControlNet for conditioned image generation, supposing an unconditioned 3D molecule generative model was already trained as a prior model, then GrCN is designed to include a 3D shape constrain module and a 3D molecule generation module as shown in Figure , where the 3D generation module is a copy of the prior model with locked parameters 𝜃 and the constrain module contains another locked copy of the prior model and a trainable copy with parameters 𝜃 ) . The trainable copy in constrain module takes a condition 3D graph 𝐺 ) as input. Within the GrCN model, the locked parameters preserve the features in the prior model learned with millions of molecules, while the trainable copy infuses conditioning graph information into prior model to guide the output 3D graph in a profound way. The output of constrain module is shown as: