We first explored the interpolation to be a conditional generation strategy. Generally, interpolation is not a typical conditional generation method because the attributes of the acquired molecules are not set explicitly, although the attributes of generated samples lying in the trajectory of interpolation are certainly influenced by these two endpoints. The transition of the attributes is also non-linear and the exact value of certain attributes can not be accurately estimated in the high dimensional latent space. However, the condition for the generated samples is a binary property here. When a proper threshold is set, the generated samples with a certain level of probability of shape persistence are considered eligible POCs. By ensuring that the trajectory of interpolation starts from a molecule with a high probability of shape persistence, the samples close to the starting molecule in the trajectory also have a high probability of shape persistence due to the structure in the latent space. The predicted probability of shape persistence is then compared with the predefined threshold to decrease the number of false positive samplings. We used spherical linear interpolation (slerp) as the default interpolation method (see Supplementary Information Section 5.3). The probability threshold was set to 0.8 (the predicted probability of shape persistence should be at least 80%) to ensure a robust sampling result.