## 3 Results and Discussions  
**Data curation results. ** Firstly, it was essential to analyze the two output files--_NoDuplicates.csv_ and _DupConflict.csv_--obtained from the data curation process using the Ecotox-curator tool. From the original dataset, 1150 non-duplicated and unique data points were retrieved, pertaining to different experimental conditions, chemical structures, and response variables. These curated data points (Table S1) were utilized for model development and validation. Specifically, 88 unique chemical structures of PPCPs were found, along with the five different experimental elements considered (i.e., \(st\), \(sg\), \(te\), \(co\), and \(ep\)). By ascertaining a toxicity cut-off of 5 mg/L, the dataset for mt-QSTR modeling comprised 603 toxic and 547 non-toxic data points. As such, the dataset is well-balanced for classification modeling. **Development and validation of mt-QSTR models. ** Following the strategy previously outlined, we began by employing the \(k\)-MCA technique to divide our dataset into sub-training (\(N_{\text{str}}\) = 641), test (\(N_{\text{ts}}\) = 161), and validation (\(N_{\text{vd}}\) =348) sets. We then first developed linear discriminant analysis models by applying the three types of feature selection techniques, i.e., FS, SFS, and GA. Multiple SFS-LDA models were built by adjusting scoring functions and cross-validation methods. The statistical results of the eight obtained LDA models are summarized in Table 2, based on their MCC values for the sub-training and test sets. As seen, the GA-LDA method-generated Model L08 provides the maximum average MCC values for the sub-training and test sets. This model was subsequently used to determine its external predictivity against the validation set, where it achieved an MCC value of 0.589. The detailed statistical results of Model L08 are presented in Table 3.  
From Table 3, it is evident that the model achieves an overall accuracy of approximately 86%, correctly predicting 989 data points. However, the external predictivity towards the validation set remains only moderate. Therefore, to ensure that L08 represents the optimal mt-QSTR model, it was crucial to investigate alternative model development strategies. This involved exploring non-linear modeling approaches, employing the five machine learning techniques (\(k\)NN, RF, SVC, MLP, and GB) along with the six feature selection algorithms (descriptors from L08 model, all descriptors, RFE, RF-importance, SFS, and GA-\(k\)NN) as detailed in the previous section. In so doing, 30 non-linear models were developed, and rigorous hyperparameter optimization was performed for each to identify the most suitable model parameters. The performance metrics for these models are summarized in Table 4. While six non-linear models demonstrated superior predictive performance compared to the best linear model (L08) on the sub-training and test sets, this advantage did not translate to improved performance on the independent validation set. This observation suggests a potential for overfitting within the non-linear models, hindering their ability to generalize beyond the training data and thus limiting their ability to surpass the predictive accuracy of the linear model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model a**} & \multirow{2}{*}{**FS Technique**} & \multirow{2}{*}{**Scoring**} & \multirow{2}{*}{**CV**} & \multicolumn{2}{c}{**MCC**} \\ \cline{5-6}  & & & & **Sub-Training** & **Test** & **Average** \\ \hline L01 & FS & — & — & 0.742 & 0.712 & 0.727 \\ L02 & SFS & Accuracy & 0 & 0.815 & 0.713 & 0.764 \\ L03 & SFS & AUROC & 0 & 0.748 & 0.773 & 0.761 \\ L04 & SFS & Accuracy & 5 & 0.777 & 0.722 & 0.750 \\ L05 & SFS & AUROC & 5 & 0.745 & 0.772 & 0.759 \\ L06 & SFS & Accuracy & 10 & 0.745 & 0.722 & 0.734 \\ L07 & SFS & AUROC & 10 & 0.754 & 0.772 & 0.763 \\
**L08** & **GA** & — & — & **0.783** & **0.811** & **0.797** \\ \hline \hline \end{tabular}  
* The most predictive model found is highlighted in bold.  
\end{table}