To further investigate the performance of the DeepSVDD model which obtained the highest MFD value of 0.59 on unseen data, we employ two additional approaches. In Figure , a comparative analysis is presented, contrasting the performance of the DeepSVDD model with that of a poor IForest model trained with 6 elemental metal features and 1613-dimensional linker features from Mordred, with an MFD value of 0.08. In the first approach, we randomly remove 20% of the ground-truth data to create a positive validation dataset and use the remaining 80% of the data to train the models. The score distributions for the positive Having trained a model to predict MOF synthesisability from a given metal and linker combination, we combine its predictions with the porosity predictions for the query dataset generated using the random forest sequential models . The synthesisability predictions using the best-performing DeepSVDD model and the predicted Pore Limiting Diameters (PLDs) range for those combinations predicted to be synthesisable are shown in Figure . By combining the synthesisability predictions and the porosity predictions, a list of combinations predicted to possess both high synthesisability and high guest accessibility is obtained.