## 4 Results
### AD Definition during ML Model Deployment (Substudy 3)  
In this last substudy, the objective is to show a concrete deployment of the developed ML-QSPR models to new species, including the predictions and the analysis of the reliability of the predictions with respect to the AD. As mentioned earlier, the new species are classified into four categories: A, B, C and D. It is necessary to divide the species into different categories to limit the loss of information. More concretely, one important limitation of descriptor-based approaches is their applicability when at least one descriptor cannot be calculated for the new query species. This is even more likely to happen with the models developed in this work, as they were trained on a large variety of chemical families and they are now being used to screen various types of species. Table 5 shows the number of descriptors that could not be calculated among the 100 selected by the GA method in each category of new species and each of the five train/test splits. Figure 17: Analysis of the causes of the high prediction errors for **enthalpy** after outlier elimination (Layout 3) and dimensionality reduction (GA). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Property** & **Category of Species** & **Split 1** & **Split 2** & **Split 3** & **Split 4** & **Split 5** \\ \hline \multirow{4}{*}{H} & A & 1 & 0 & 0 & 1 & 2 \\  & B & 7 & 8 & 5 & 4 & 4 \\ \cline{1-1}  & C & 18 & 9 & 13 & 12 & 21 \\ \cline{1-1}  & D & 12 & 16 & 11 & 12 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Number of non-computable descriptors (among the 100 descriptors selected by GA) for each category of new species. It can be observed that, for some categories of species, the number of impossible-to-calculate descriptors becomes significant before the total number of considered descriptors, reaching percentages in the order of 20% to 25%. This is, for example, the case for categories C and D, especially for the entropy calculation. Indeed, several species in these categories contain a specificity that prevents the calculation of several descriptors. Note that, in the DIPPR data that were used for the model construction, no species with this specificity were present. As such, the 100-GA descriptors, identified via the dimensionality reduction step, do not necessarily represent these species adequately. One possible solution would have been to develop a ML model based on data enriched with similar species as those in categories C and D; however, they were not readily available during the study. Another factor preventing the calculation of certain descriptor values for Cat. C species is also related to the presence of specific atoms [54]. In fact, a posterior analysis has revealed that the few molecules that exist in the DIPPR database, containing these specific atoms, were eliminated during the preprocessing stage, either due to missing property or descriptor values (cf. first article of the series [15]). Consequently, the ML/OSPR models needed to be retrained on the basis of the proportion of the 100 descriptors, selected using the GA method, which could be calculated for the new species, before implementing the model for the prediction of their properties. This turned out to be problematic in the case of category C, as the significant elimination of descriptors resulted in duplicate molecules in the training set (i.e., molecules that could no longer be differentiated on the basis of the remaining descriptors). The parity plots for Split 1 are presented in Figures 18 and 19 for the enthalpy and entropy, respectively. In Figure 18c, it is interesting to observe that the points showing higher distances to the y = x line correspond to species containing atoms that were not represented in the training data, as discussed earlier. The detailed predictions of the enthalpy and entropy of all the new species, according to various splits, are provided in the Supplementary Materials.