However, once we add more noise (noise level of 0.4) on the mixed point cloud model, it (with the core of 0.583) out-performed the point cloud model. The shape similarity and 2D graph similarity of generations under various shape conditions are displayed in Figure . In general, the stricter shape condition used, the higher 2D similarity is between the generated compound set and the reference. For the fuzziest option 'Mixed Point cloud' with noise of 0.4, its median shape similarity is 0.823, while the median 2D similarity is 0.187, which means that our model is capable in generating compounds with high shape similarity while keeping low 2D similarity. It is worthwhile to note that for mixed point cloud models under noise level of 0.3 and 0.4, the fuzzier model (noise level of 0.4) tends to generate more novel structures, while its shape similarity decreases slightly. Given these results, mixed point cloud model was chosen for doing following experiments in current study. 2. The performance of Diff-Shape on shape-conditioned structure generation.