Plausibility checks for infrared spectroscopy check if those functional groups, which are part of the chemical target structure, can be identified in a provided IR spectrum (option 5, Figure ). Our evaluation process utilises available cheminformatics toolkits such as nmrglue , rdkit and functional group finder (IFG) in combination with an ML model. The implemented model adopts convolutional neural networks to recognise the presence of functional groups from the full spectrum profile, not discrete peaks. This method is data-driven without pre-encoded rules (see also results section). The trained model is used to estimate whether IR data files contain the signals expected due to the functional groups of the corresponding chemical structure. A detailed description of the methods and results for the implementation of the model is given in the Supporting Information (starting with Chapter 4). Based on this model, the functional groups of the molecules to be curated are extracted from the given Molfile, and spectroscopic data points are extracted and preprocessed using the IR JCAMP-DX file. For each functional group that is expected, the outcome of the ML model in the form of the probability (does the spectrum reveal the presence of a functional group?) as well as the typical confidence of the model with respect to the functional group (referring to the test data) is given. Based on a combination of these two indicators, an assessment of the matching of experimental and predicted data as an indicator for the plausibility of the data is made (Figure ). Depending on the confidence of the model for a certain functional group and the machine's result, the curator can follow the evaluation or adapt the outcome manually.