A range of rapidly evolving machine learning (ML) and deep learning (DL) approaches have been extended to multiple scientific areas with the development of improved computational hardware and capabilities. The development of ML and DL algorithms offers a solution for predictions of large-scale molecular systems involving high dimensional feature space, where the conventional computational approach becomes impractical. The applications of ML have covered a wide variety of topics within chemistry and material science such as drug discovery, retrosynthesis planning, and acceleration of theoretical calcu-Figure : A collection of experimentally reported POCs with different topologies and formed from different reactions lations. In the discovery of POCs, ML has been applied to make predictions. For example, the prediction of the porosity of porous molecular materials based on crystallographic data. We have previously used ML models for property prediction, specifically POC shape persistence. To do so, we created a dataset of more than 60000 POCs assembled in silico from a variety of di-, tri-and tetra-topic building blocks, using our supramolecular toolkit software, stk, which is a python library for modelling supramolecular chemistry. The random forest algorithm exhibited high accuracy in the discrimination of shape-persistent cages. Later on, an improved DL model, a graph neural network (GNN), was developed to predict shape persistence with the combination of molecular graph representations. Compared with the previous model, graph neural networks not only exhibited better performance, but improved model explicability. Discriminative ML models, aimed at modelling the conditional probability of the property of the given input data, are limited in the exploration of existing chemical space. Instead of learning the mappings from molecules to their properties, generative models can model the distribution of input molecules and depict the chemical space itself. Therefore, generative models are capable of generating synthetic molecules that have a similar distribution to the input molecules. As the generative model can produce results beyond the instances in the input, it is possible to use this approach to automatically expand the conventional chemical search space through de novo molecule generation without human intervention. In addition, the generative process can be subject to bias signals from one or several properties of interest, making it property-constrained. Contemporary generative models are typically based on DL due to the strong comprehensive performance of multi-layer neural networks.