In the past, various algorithms, models, software tools, and web-services have been described that can be used for efficient, (partially) automated processing and curation of data in the discipline of synthetic chemistry. The necessary input for those systems is generally the chemical structure in combination with either the data file or the textual description (hereinafter referred to as named "analysis") of the measurement. For NMR measurements of organic molecules, there are several established rule-based systems available. nmrshiftdb2 offers a web service that can be used to compare NMR shifts of experimentally gained 1 H NMR and C NMR data with chemical shifts that are simulated based on the chemical structure of the expected molecule. Other examples for software and services that can aid curation efforts for NMR data are CSEARCH , so-called CASE software (computer-aided structure elucidation) such as seneca or LSD , CASPER (computer-assisted spectrum evaluation of regular polysaccharides) or the compound-class agnostic tool "Ask Ernö" . ML techniques have been applied to NMR signal processing, prediction, and structure verification. Several approaches use DFT-computed data as input for the training of (deep) neural networks gaining suitable models to predict C NMR shifts to e.g. identify, structural mis-assignments of organic compounds. Other work with neural networks has accelerated the development of suitable simulation methods for 1 H NMR and C NMR-based shifts extracted from experimental data in the last years. For the simulation of IR spectroscopic data, DFT calculations can be used for specific scientific challenges and general approaches were described in previous work. However, several ML approaches for the simulation of IR-vibrational data were also reported. Also web services were built to facilitate the comparison of experimental IR data with previously reported data and the analysis of given spectra and the prediction of spectra from a given structure. ,