In addition, the numbers in each unit, representing the probability of collapse of the cage molecule, have a monotonic increase in both interpolations. This demonstrates that the latent space is also effectively organised by the external shape persistence predictor. The current latent space is arranged cooperatively by features captured by both the VAE and the predictor. Compared to linear interpolation, spherical linear interpolation often results in an uneven sampling in the trajectory where the sample in the middle region is sparse, reflected by the probability value of shape persistence predictions. However, it may indicate that slerp finds it easier to obtain POCs with probability values close to the lower and upper limits in the prediction of shape persistence. In a binary predictive model, probabilities approaching the lower and upper limits typically denote a higher degree of confidence in the assigned label, which indicates that the predicted shape persistence of sampled novel POCs by slerp is potentially more robust.