Six common classifiers available in the sklearn python module were chosen: k-nearest neighbors (KNN), decision tree, SVC, extra trees, random forest, and gradient boosting. These classifiers had their hyperparameters optimized and were trained in a database containing relevant H NMR spectral features (chemical shift, J-coupling values, integral values, and multiplicity). This database was created from examples found in NMR Challenge , an online platform with several exercises to improve NMR spectral analysis skills kept by Martin Dracinsky's group at IOCB Prague, NMRium 9 , a broader online platform where exercises for structural determination are present alongside tools for spectral analysis, and the features predicted by NMRium 1 H NMR predictor for amino acids . From these spectra, 1 H chemical shift values, Jcoupling values (limited to first coordination sphere), integrals, splitting patterns, and their respective structural groups were taken. Structural groups present in the database are alkane (CH3R, CH2R2, CHR3), aromatic, ether (RCH2OR, R2CHOR), ketone (CH3COR, RCH2COR, RCHCOR), N-alkane (alkanes bonded to nitrogen atoms), vinylic (R2C=CH2, R2C=CHR), benzylic (aromatic-CH3, aromatic-CH2R, aromatic-CHR2), aldehyde, carboxylic acid, and alcohol (RCH2OH, R2CHOH). A summary of the occurrences for each structural group are shown in Figure . As one can see, our definition of the structural groups is rather general but, even with this approach, there is a prominent difference in occurrence between the alkane group (with more than 175 occurrences in the database) and other groups such as alcohol or carboxylic acid (with less than 20 occurrences each). This difference causes the database to be classified as imbalanced and oversampling techniques are necessary to produce good classifiers. Our database was oversampled using Synthetic Minority Oversampling Technique (SMOTE) , two of its variations (Borderline SMOTE and SVMSMOTE 13 ), and Adaptative Synthetic Sampling (ADASYN) . These methods were selected because they are easily accessible through the imblearn python module . Each minority group was oversampled to 70 occurrences using a KNN algorithm, where synthetic data was only created near k=5 datapoints from the database. With this, a ratio of 2.7:1 was reached for the low-occurrence groups when compared to the alkane group. The oversampled database used to train the classifiers had 915 entries, being 662 original entries and 253 entries generated by the above-mentioned oversampling methods. 75% of the oversampled database was used as the train partition while 25% was used as the test partition of the oversampled database.