More specifically, the model has very high sensitivity for nitrogen (N) and oxygen (O) which are the most common atom species in the dataset (excluding carbon which is not included in this analysis as it is present in all molecules). However, the specificity for oxygen is significantly lower than that of nitrogen which means that there is a significant number of false positives for oxygen compared to nitrogen. Regarding the more rare atom species, the opposite phenomenon is observed: specificity is significantly high while sensitivity is low. This means that for the rare species there is a very small number of false positives which is expected as these atoms are under-represented in the training set. However, sensitivity is at least 0.5 for all atoms, which shows that the model is able to capture the presence of rare atoms quite well considering that some atom species are severely under-represented in the training set.