The conditional generation for POCs that are shape-persistent can alternatively be achieved using molecular optimisation. Bayesian optimisation is one of the most common strategies to navigate molecular optimisation in the latent space. The objective of Bayesian optimisation is to find the best POCs that meet the required condition depicted by the acquisition function. The exploration and exploitation of the Bayesian optimisation were balanced by adding a weighted regularisation term based on a standard normal distribution. We use the following acquisition function to achieve the latent representations of shape-persistent POCs: