In this study, we comprehensively evaluate a diverse range of features to effectively represent materials for the synthesisability problem, including three different sets of metal features and three distinct groups of linker features, as shown in Figure . Various OCC algorithms are examined, encompassing five well-established classic approaches as well as two advanced neural network-based methods. Ultimately, the MFD is proved to be an effective metric to assess the performance of OCC models. We report a deep-learning based model with superior capability to predict the likelihood whether a MOF can be synthesised given a combination of any single metal and single linker. This model correctly predicts that 12 of 14 unseen negative samples cannot be synthesised. The synthesisability prediction using the best-performing model and the porosity prediction using random forest sequential models are conducted concurrently on the query dataset. Out of the entire set of 160,582 metal and linker combinations, 3,453 are predicted to be synthesisable and to have a high Pore Limiting Diameter (PLD), indicating their higher probability of providing a large guest-accessible space and being successfully synthesised.