With the advances in machine learning methods, it was just a matter of time before they were used to process data, aid with data interpretation, and provide structural predictions based on features from spectroscopic techniques . For solution-state NMR, only a few methods are available, ranging from commercial software such as MNova or ACD/Labs, to free codes like CReSS 2 (cross-modal retrieval between spectrum and structure) and CMGNet (conditional molecular generation net). These free methods are examples of computer-assisted structure elucidation (CASE) and are improved library-matching methods. Although effective, they require a high computational power to match the experimental spectral features with those in available databases such as NMRShiftDB and/or NAPROC-13 . An alternative is NMR Fingerprint 6 , a free tool based on Supported Vector Classification (SVC) that uses 13 C and 1 H chemical shifts alongside 13 C DEPT90 and DEPT135 peak intensities as input, to identify structural groups present in an unknow molecule. Albeit this tool does not predict the structure responsible for a given NMR spectrum, it gives a good compromise between a free code that may not work and a time-consuming task.