## 3 Results and Discussions
 } & \multicolumn{4}{c}{**MCC**} \\ \cline{3-6}  & & & **Sub-Training** & **Test** & **Average a** & **Validation b** \\ \hline NL01 & L08 descriptors & RF & 0.813 & 0.824 & 0.819 & 0.469 \\ NL02 & & \(k\)NN & 0.739 & 0.713 & 0.726 & ND \\ NL03 & & SVC & 0.745 & 0.811 & 0.778 & ND \\ NL04 & & MLP & 0.780 & 0.736 & 0.758 & ND \\ NL05 & & GB & 0.802 & 0.773 & 0.788 & ND \\ NL06 & All descriptors & RF & 0.824 & 0.864 & 0.844 & 0.527 \\ NL07 & & \(k\)NN & 0.683 & 0.646 & 0.665 & ND \\ NL08 & & SVC & 0.414 & 0.460 & 0.437 & ND \\ NL09 & & MLP & 0.673 & 0.710 & 0.692 & ND \\ NL10 & & GB & 0.827 & 0.849 & 0.838 & 0.517 \\ NL11 & RFE & RF & 0.809 & 0.773 & 0.791 & ND \\ NL12 & & kNN & 0.745 & 0.773 & 0.759 & ND \\ NL13 & & SVC & 0.764 & 0.773 & 0.769 & ND \\ NL14 & & MLP & 0.733 & 0.723 & 0.728 & ND \\ NL15 & & GB & 0.807 & 0.798 & 0.803 & 0.469 \\ NL16 & RF-importance & RF & 0.811 & 0.823 & 0.817 & 0.515 \\ NL17 & & kNN & 0.701 & 0.772 & 0.737 & ND \\ NL18 & & SVC & 0.755 & 0.760 & 0.758 & ND \\ NL19 & & MLP & 0.767 & 0.747 & 0.757 & ND \\ NL20 & & GB & 0.796 & 0.735 & 0.766 & ND \\ NL21 & & FSS & RF & 0.746 & 0.79 Moreover, establishing the applicability domain of the model is also deemed necessary. When applying the standardization approach, 31, 11, and 25 data points were detected as structural outliers for the sub-training, test, and validation sets, respectively, and their removal led to MCC values of 0.772, 0.826, and 0.593, respectively. The confidence estimation approach, which may be more important for outlier determination in mt-QSTR modeling, especially for the validation set, estimates the level of confidence with which a model may predict a data point. Using a threshold value of 0.25, 48 outliers were detected in the sub-training set, and their removal increased the MCC score to 0.802. The test and validation sets had 17 and 36 outliers, respectively, and removing these outliers resulted in new MCC values of 0.83 and 0.63, respectively. The significance of the descriptors of the L08 model was estimated by their standardized coefficients, the results of which are depicted in Figure 3. The meaning of these deviation descriptors is outlined in Table 5.  
Figure 3: Contribution of deviation descriptors in the linear L08 model. Figure 2: ROC plot of the L08 model (**A**) and inter-correlation among its deviation descriptors (**B**). Noticeably, save for the endpoint (_ep_), all experimental elements selected in the present work were found in the mt-QSTR model, indicating the importance of each. However, the 'Species scientific name' (_sn_) appeared the most frequently (five times), while both the 'Time of experiment' (_te_) and 'Concentration type' (_co_) were found in two deviation descriptors each. Meanwhile, the 'Species group' (_sg_) was found in only one deviation descriptor. The \(\Delta\)(Ms)\({}_{sn}\) descriptor, relating to the experimental element _sn_, indicates that a higher value of this descriptor is responsible for lower aquatic toxicity. Generally, the presence of polar atoms (such as N, O, F, Cl, etc.) and unsaturation increases the value of Ms in compounds. In Figure 4, some correctly predicted PPCPs with low and high values of \(\Delta\)(Ms)\({}_{sn}\) are presented. Naturally, compounds with fewer polar residues (compared to the total number of atoms) were found to have lower values of this descriptor. Evidently, compounds with low Ms values may have a higher chance of increased lipophilicity due to the lack of electronegative atoms. This is further corroborated by  
Figure 4: Examples of data points to explain the contribution of \(\Delta\)(Ms)\({}_{sn}\) to aquatic toxicity.