The 1M1L3D dataset used to train models is obtained from Pétuya et al. This dataset is sourced from decomposing reported experimental three-dimensional MOF structures made of a single metal and a single linker in CSD into their constituent components, expressed as metal symbols and canonical simplified molecular input line-entry (SMILES) strings. As shown in Figure (steps 1-2), the 1M1L3D dataset consists of 14,296 MOFs with unique refcodes (entries) in total. Among these data, there are 6,921 duplicated [metal, linker] combinations with multiple CSD entries. In this case, 7,375 unique [metal, linker] pairs remained that are known to produce MOFs and can be directly used for model training, denoted as the "Groundtruth" dataset. Any combination of metal ions/clusters and organic ligands can be attempted to synthesise a MOF structure. For this study, we exclusively use metal ions/clusters and organic ligands from the reported structures to generate a query set. Thus, the dataset encompasses 53 distinct metals and 3,169 distinct linkers, resulting in 53 × 3,169 = 167,957 possible [metal, linker] combinations. Only 7,375 of them have been reported to produce MOFs. By training our model on these "ground-truth" positive examples, we can identify the most promising [metal, linker] combinations from the remaining 160,582 possibilities (also denoted as the "Query" dataset).