The early attempts on shape conditioned generative model were to build models not directly generating 3D conformations. Skalic et al. and Imrie et al. trained networks to generate 1D SMILES strings and 2D molecular graphs, respectively, conditioned on CNN encodings of 3D pharmacophores ignoring Euclidean symmetries. Zheng et al. used supervised molecule-to-molecule translation on SMILES strings for scaffold hopping tasks and evaluated the generated scaffolds' 3D shape similarity to the reference. Papadopoulos et al. sampled molecules with high shape similarity to a target by SMILES based reinforcement learning in REINVENT, requiring re-optimization of the agent model for each target shape. Roney et al.