Computational approaches to facilitate the design of functional molecules include the development of de novo algorithms that explore chemical spaces to generate new compounds. For instance, the de novo design algorithm for exploring chemical space scans the space and generates structures in a specific area on a user-selected pane . Similarly, Capecchi et al. developed the peptide design genetic algorithm (PDGA), a computational tool that generates highly-similarity analogs of bioactive peptides with various peptide chain topologies in a chemical space defined by the macromolecule extended atom pair fingerprint . Recently, Aspuru et al. proposed the superfast traversal, optimization, novelty, exploration, and discovery (STONED) algorithm to perform exploration and interpolation in chemical space to obtain novel molecules . STONED uses self-referencing embedded strings , a molecular representation that is more suitable for ML. This algorithm reduces the long training times, large datasets, and handcrafted rules.