## 2 Materials and Methods
The parameters optimized for the various ML techniques are described in Table S2. A 5-fold cross-validation was employed for both hyperparameter optimization and model evaluation. A random seed value of 42 was used consistently throughout the process. Finally, we evaluated the predictive performance of the optimized models on both the test and validation sets. **Model evaluation and reliability. ** The predictive performance of the final classification models was assessed using a range of well-established statistical parameters, calculated by the QSAR-Co-X tool. Formulas for these parameters--sensitivity, specificity, accuracy, and [PERSON]'s correlation coefficient (MCC) [19; 29; 30]--are given in Table 1. For model selection, we primarily relied on MCC, which is considered a stringent and reliable metric for evaluating the predictive quality of classification models, especially when dealing with imbalanced datasets. Additionally, receiver operating characteristic (ROC) curves were generated to provide a graphical representation of model performance. Essentially, the ROC curve plots the true positive rate (TPR) against the false positive rate (FPR) across various classification thresholds. The area under the ROC curve (AUROC) was also calculated, offering a quantitative measure of the model's ability to distinguish between classes. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Equation** \\ \hline Sensitivity (Se) & \(\frac{Number\ of\ true\ positives\ (TP)}{Number\ of\ true\ positives\ (TP)+Number\ of\ false\ negatives(TN)}\) \\ Specificity (Sp) & \(\frac{Number\ of\ true\ negatives\ (TN)}{Number\ of\ true\ negatives\ (TN)+Number\ of\ false\ positives\{FP\}}\) \\ Accuracy (Acc) & \(\frac{Number\ of\ total\ datapoints\ (N)}{Number\ of\ false\ positives\ (TP)}\) \\ Mathew’s correlation coefficient (MCC) & \(\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Formulas for key statistical parameters used in the present work to evaluate the quality of classification-based mt-QSTR models. Furthermore, we employed two complementary techniques for determining the applicability domain (AD) of our mt-QSTR models. First, the standardization-based AD method, proposed by [PERSON] et al. [31], was utilized. This method identifies outliers and confirms if predictions remain within the defined model domain. All calculations related with this method were performed with the QSAR-Co-X software. Second, we applied the confidence estimation approach, also developed by [PERSON] et al. [32], with a threshold value of 0.25. This technique assesses the reliability of predictions based on their proximity to the training data. Both methodologies have been comprehensively explained in a previous study [20], providing robust measures to evaluate the reliability and applicability of the developed mt-QSTR models. **Consensus modeling. ** Recent years have seen the introduction of the concept of consensus modeling, which posits that combining the most predictive models will invariably enhance model performance, ensuring the final model's maximum predictive capability [7]. In this work, we employed the newly introduced 'Module5.py' module within QSAR-Co-X software to automatically perform consensus modeling on our models. It is important to note that when using an odd number of models, the software employs a majority voting system to handle predictions with uneven distributions. However, for an even number of models, this method is only applicable when there are at least four models, and the predictive distributions are not uniform. For an even number of models with 50% predictions as 'toxic' (or +1) and 50% as 'non-toxic' (or 0), such rule is not applicable, and predictions are based on posterior probabilities. The prediction with maximum posterior probability is then considered as the final prediction.