Parallelly, the molecular similarity metrics combined with representative fingerprints had been used to cluster chemical entities (e.g., clustering based on common scaffolds) from diverse chemical datasets, covering a crescent relevance in modern molecular design. Thus, recent advances in artificial intelligence chemistry (e.g., machine and deep learning technologies) have optimized through the use of clustering algorithms. And, again exists a plethora of applications to develop organic molecules against different chemical-biological-industrial contexts. However, the clustering algorithms have not been applied in dept in development of metal-based compounds, which could explained by the current fingerprint limitations. In this regard, in this work, we applied the metal-based fingerprints development to create cluster representation based on a distance matrix generated by the paired Jaccard distances of each compound in the MetAP DB dataset. For example, Figure shows the Jaccard distance (1 -similarity) of each pair of compounds contained in each of the five specific clinical categories considered in this work. Where, Metal-FP3 exhibited a higher capability to differentiate the electronic, atomistic, and structural changes in the MetAP dataset, followed by Metal-FP2, and Metal-FP1, respectively. Interestingly, the metal-based compounds used in imagenology exhibit constant lower distances between them (higher similarity), independently of the fingerprint used to codify their structures. Compounds containing the categories radiotherapy and photodynamic therapy were the compounds with higher distances between them (molecular diversity), which remark on the impact of fingerprints to identify higher distant compounds (i.e., less similar compounds), some representative examples are illustrated in For each structure, the *.sdf file was obtained from , and these were minimized using the MMFF94x forcefield implemented in the Molecular Operating Environment (MOE) software.