## 4 Results
### AD Definition during ML Model Construction (Substudy 2)
#### 4.2.3 On the Understanding of the High Prediction Errors for Test Molecules
In this sense, one possible explanation--which is, for example, corroborated by the test molecules with the highest prediction errors in splits 4 and 5--is related to the isolation of these molecules from other \"similar\" ones in the training data. For example, in split 4, the molecules with chemid n\({}^{\circ}\)3965 and n\({}^{\circ}\)899, which are inorganic compounds, are close only to a few training molecules that are completely different in structure (i.e., esters/ethers and nitriles), thus displaying a significantly different property value. Another similar example concerns the amine molecule n\({}^{\circ}\)1735 and the inorganic molecule n\({}^{\circ}\)1929 in splits 2 and 5, which are located within a dense region composed of chemically diversified compounds (halogen and inorganic compounds for the first molecule; aromatics, epoxides and esters/ethers for the second one), which have large differences in property values. Another possible explanation for the observed high prediction errors of some test molecules concerns molecules that are found completely isolated on the graphs, as a result of a low representation of their characteristics (i.e., as perceived by the model on the basis of the used descriptors) in the dataset. An example is the molecule n\({}^{\circ}\)922 in Split 3. Finally, it is also observed that some clusters are formed by molecules that display high variation in their enthalpy value, although the value is identified as \"similar\" by the model. This can lead to underfitting and poor prediction performance in these areas and can be due either to the uncertainties that might exist in the reference property values in the DIPPR database, or to the incapacity of the (selected) descriptors to capture the complete spectrum of structural differences of the molecules that are associated with the measured property. In this sense, different approaches exist in the literature where the use of descriptors is replaced by alternative molecular representation methods, such as graph neural networks (GNN), each one displaying its own advantages and drawbacks [75; 76; 77]. The fact that test molecules, having close training neighbors (in tSNE 2D projection) with low variance in their property values, are generally well predicted can be more clearly visualized in Figure 17. Here, again, the results shown are only for splits 2 to 5 for the enthalpy, but the results for splits 2, 4 and 5 for the entropy are also available in Figure A6. In particular, Figure 17 shows, for each test point, the average distance to its five nearest training neighbors and the standard deviation in the property values of these same neighbors, in relation to the prediction error of the test point. What is observable is that most points are located on the bottom-left corner, which confirms that well-predicted test points are generally those that have close training neighbors with low variance in their property values. However, a high distance to training points and/or a high variance in the property value of closest training points do not always lead to poor predictions, while, in some rare cases, molecules displaying relatively small distance to their five nearest neighbors and small standard deviation in the property values of these neighbors, are poorly predicted by the model. This is, for example, the case of the molecule n\({}^{\circ}\)4991 in Split 2 or the molecule n\({}^{\circ}\)1692 in Split 3. Further analyses are needed to better explain this contradicting behavior, but the previously emitted hypotheses can also apply here, including the unadapted descriptor-based representation, the imperfect tSNE 2D representation, the lack of training data in some regions, the AD representation ignoring the influence of the descriptors on the model, the presence of property cliffs, or the uncertainties in the DIPPR property values.