To address this, there have been efforts to build ML systems capable of decoding spectra directly into molecular structures. For example, Alberts et al. showed an example of direct "translation" of C NMR and H NMR to molecular structures. However, these techniques still do not reflect the reality that chemists must flexibly combine many different spectroscopic techniques for structure elucidation. In addition, for practical applications, the predictions of such a system must provide a relevant context (e.g., the most relevant literature examples) and a confidence estimate to be practically used by chemical scientists. As an alternative to fully data-driven approaches, attempts have been made to use substructure-based rules. Similar to many conventional computer-assisted structure elucidation pipelines (CASE), these systems often require pre-processing of spectra, such as peak picking. Moreover, automated structure elucidation pipelines would be even more useful if the knowledge base that the system uses to make inferences could easily be adjusted to new datasets (e.g., in-house electronic lab notebooks) without model retraining.