One of the key questions in computational chemistry, particularly in chemoinformatics, is how to create valid and useful representations of different types of molecules. Actually, the development of current molecular representations have been promoted by the combination of methods, descriptors, and features to simplify the most important data to represent each kind of molecular data set. This has occurred in chemoinformatics and many other areas due to the need to organize more information in a simple format, which can be used to generate new information from new artificial intelligence (AI) methods (i.e. machine and deep learning). A clear example is the evolution of the Morse code, through the traditional barcode, which enabled the creation of its two-dimensional representation (QR codes) with greater capacity to store data, as illustrated in Figure . Something similar happened in chemoinformatics which the availability of new experimental or predictive features must be condensed in a unique representation. For example, simple chemical structure representations (e.g., molecular formula), which uniquely simplify atomic data of chemical structures, have laid the groundwork for creating string-based molecular representations capable of encoding functional groups and atom-connectivity information into linear notations (e.g. SMILES, SMARTS, and InChi keys) . Which in turn led to the creation of bit-based molecular representations capable of being used to analyze these structures with high-performance computing (e.g. MACCS keys fingerprints) . In the same way as traditional barcodes, bit-based molecular representations evolved to contain more information, for example, related to their connectivity and chirality (e.g. extended connectivity fingerprints -ECFPs), which was originally designed to generate a unique code for molecules . This allows to establish reproducible chemoinformatics algorithms for substructure searching , structureproperty modeling, and proved to be useful representations to construct ML tasks to predict multiple properties . On the other hand, and similarly to traditional barcodes, bit-based molecular representations have limitations related to their ability to create unique codes for each molecule. This need inspired the creation of new bit-based molecular representations using ML approaches, like DeepSMILES , and SELFIES ). SELFIES, which has been associated with fewer generating rates of invalid molecules. This allows to convert every string into a valid molecule .