We recently proposed a diffusion model, LigandDiff, to design novel ligands for octahedral TMCs. LigandDiff introduces the scaffold technique to the diffusion model and allows for the generation of 3D monodentate or polydentate ligands at a given coordination site. Specifically, given a metal center with several connecting ligands (context u), LigandDiff generates one appropriate ligand for the vacant coordination site to form a complete complex. But since the initialization of the reverse process is random, even with the same u, numerous ligands with diverse configurations can be designed. In LigandDiff, each complex is denoted as 𝑥 = [𝑟, ℎ ! , ℎ " ], where r is the coordinate of each atom, ℎ ! is the one-hot embedding of atom type, and ℎ " indicates the ligand group information, i.e., which atoms are from the same ligand. In the forward process, an assigned well-structured ligand 𝑥 # " in each complex is deconstructed to a group of isolated atoms 𝑥 $ " . The unstructured data is then used to parametrize a neural network 𝜙 to estimate the noise at a given step t, i.e., the distance between 𝑥 % " and 𝑥 # " . To generate a new ligand, LigandDiff starts from 𝑥 $ " , sampled from a prior distribution, and removes the noise predicted by parametrized 𝜙 to reverse 𝑥 $ " to 𝑥 $&' " . By iterating this denoising process from 𝑥 % " to 𝑥 %&' " , finally a new ligand 𝑥 # " is generated. The overview of LigandDiff is given in Figure . With this extra embedding, users can customize the ligand denticity of each generated ligand.