A more direct approach to the structure elucidation problem would be to reconstruct the underlying chemical structures given spectra features. Such an undertaking though is computationally challenging as it requires the generation of a molecular structure. Indeed, this approach is performed as a two step process to circumvent the need for generating molecular structures: A machine learning model is used to map the spectrum to an intermediate vector representation such as a molecular fingerprint. Once the fingerprint is obtained then it is matched against the content of structural databases in order to identify candidate molecular structures with similar fingerprints . This method though will also fail for molecules that are not present in the structural database and especially for novel molecules. A more direct association of spectra features with molecular structures through a rule-based approach has also been explored . More specifically, this approach extracts rules, that associate spectra features with substructures, from spectra databases aiming at a partial structure identification.