fine-tuned a 3D generative model on the hits of a shape based virtual screen of more than 1010 drug-like molecules to shift the learned distribution towards a particular shape. Yet, this expensive screening approach must be repeated for each new reference shape. The first transferable shape-conditioned 3D generative model, SQUID, proposed by Coely et al., utilized auto-regressive fragment based generation with heuristic bonding geometries, allowing to predict torsion of connecting bonds of growing fragments to make sure the growing 3D conformation properly aligned to the reference shape. However, its performance was only demonstrated on small drug-like molecules up to 27 atoms. Ning et al. proposed another attempt for shape-constraint molecular generations, ShapeMol , an equivariant diffusion model conditioned with a shape-embedding from encoder, they demonstrated that ShapeMol outperformed SQUID on shape-conditioned virtual screening.