Evaluation metrics for Diff-Shape generated molecules includes the 2D graph similarity 𝑆𝑖𝑚 I and 3D shape similarity 𝑆𝑖𝑚 JK with template molecules. The 2D similarity was calculated by the Tanimoto similarity of ECFP-4 fingerprints between generations and templates, while the 3D shape similarity was calculated with the ROCS software of OE Toolkit package in default settings. For the tasks of structure-based drug design, we also evaluate the docking score of generations in target pockets with the GLIDE module of Schrodinger software package. In our study, we utilized an unconditioned MIDI model as the baseline. We conducted a performance comparison between Diff-Shape and two other shape-conditioned generative models: SQUID and ShapeMol. The λ parameter of SQUID was set to 0.3, as recommended in reference 17 for optimal performance. 17 ShapeMol was evaluated with and without guidance using a shape similarity-based score function during the diffusion process to improve its performance. These two variants of ShapeMol are denoted as "ShapeMol-NoGuide" and "ShapeMol-Guide-0.5", respectively, in which the guidance weight was set to 0.5 in the guidance mode. Furthermore, we evaluated Pocket2Mol and DiffSBDD in the context of structure-based drug design. Both models were employed with their default settings for the evaluation.