The distribution of the N3PL scores of the approved drugs encompasses all the range from the NP-like molecules, to the small molecule drugs. The distribution shows that the NP-like molecules have a higher abundance than the small molecule drugs. NP-likeness calculator and NP-Scout results showed the opposite trend: a higher abundance of synthetic-like molecules (Fig. ). This can be explained by the neural network training, the underlying training dataset and the properties of drug-like molecules. The training dataset was compiled out of the COCONUT database and SMs from the ZINC database with an Ertl score of less than zero. Interestingly, the ZINC database shows only a small number of compounds with an Ertl score greater than zero, which is in contrast to the analysis of approved drugs. This shows that approved drugs have different properties and are more NP-like than the space of synthetic available compounds. In contrast to the Ertl score, the N3PL score was not trained on specific fragments but on NP-likeness and 48 additional surface descriptors to cover the physicochemical space of NPs. Since NPs show a property profile that is particularly useful for drug discovery, it is not a surprise that developed drugs also show a similar property profile which is reflected in the N3PL score. To summarize, the NPLC score analyzes typical NP fragments and the N3PL likeness score also includes NP physicochemical properties besides structural features. This leads to a higher NP-likeness in the approved drugs. This also explains the rather smooth distribution around a score of ten in contrast to NPLC scores with often two peaks. Regarding the N3PL scores of the individual Latin American countries (Fig. . C and C1), they showed a nearly normal distribution form centered in the number ten. Thus, these compounds are labeled as NP-like. The NP-likeness score calculated with the neural networks approach has the least variation among the three methodologies in the case of LANaPDB, showing more variation in the previous two methodologies (Fig. and) and a normal distribution form with the neural networks (Fig. ). Regarding COCONUT, there is also less variation in the N3PL score (Fig. ) compared to the NP-likeness calculator (Fig. ).