## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **ML1-To** & **ML1-Tr** & **ML1-Ts** \\ \hline TP 1 & 302 & 262 & 40 \\ TN & 1681 & 1332 & 349 \\ FP & 172 & 132 & 40 \\ FN & 16 & 11 & \(5\) \\ Sensitivity & 0.950 & 0.960 & 0.889 \\ Specificity & 0.907 & 0.910 & 0.897 \\ Accuracy & 0.913 & 0.918 & 0.896 \\ MCC & 0.733 & 0.756 & 0.618 \\ F1 & 0.763 & 0.786 & 0.640 \\ AUC & 0.948 & 0.954 & 0.920 \\ G-Mean & 0.928 & 0.934 & 0.893 \\ Dominance & 0.043 & 0.050 & \(-0.008\) \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 1: Prediction results and performance evaluation metrics of the ML1 model on the total (To), training (Tr) and test (Ts) sets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **MH1-To** & **MH1-Tr** & **MH1-Ts** \\ \hline TP 1 & 300 & 261 & 39 \\ TN & 1693 & 1341 & 352 \\ FP & 160 & 123 & 37 \\ FN & 18 & 12 & 6 \\ Sensitivity & 0.943 & 0.956 & 0.867 \\ Specificity & 0.914 & 0.916 & 0.905 \\ Accuracy & 0.918 & 0.922 & 0.901 \\ MCC & 0.742 & 0.765 & 0.619 \\ F1 & 0.771 & 0.795 & 0.645 \\ AUC & 0.954 & 0.958 & 0.928 \\ G-Mean & 0.928 & 0.936 & 0.886 \\ Dominance & 0.030 & 0.040 & \(-0.038\) \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 2: Prediction results and performance evaluation metrics of the MH1 model on the total (To), training (Tr), and test (Ts) sets. Inspection of Table S1 reveals that 256 compounds (11.79%) have their repellent activity wrongly predicted by at least one model. This number drops to 110 (5.07%) when both models incorrectly allocate the chemicals to their class of activity. Among them, five are repellents predicted as non-repellents. Three belong to the training set and two to the test set (Table S1). Among the 105 non-repellents predicted as repellents by both models, 19 are test set members. There are 78 molecules having their repellent activity wrongly predicted only by the ML1 model. Among them, 24 belong to the test set (i.e., three repellents and twenty-one non-repellents) and the others are training set compounds (i.e., eight repellents and forty-six non-repellents). On the other side, 68 molecules are incorrectly classified only by the MH1 model. Among them, 22 are test set molecules (i.e., four repellents and eighteen non-repellents). The remaining compounds belong to the training set, including nine repellents and thirty-seven non-repellents (Table S1). Thus, the MH1 model shows a small advantage compared to the ML1 model having slightly fewer bad predictions. Whatever the differences of behavior between the ML1 and MH1 models, inspection of Tables 1, 2 and S1 shows that both models have good prediction performances and behave similarly for a lot of chemicals. These are reinforced when the models are used for predicting the repellent activity of the main commercial repellents [51; 52]. Indeed, it is satisfying to note that the ML1 and MH1 models correctly allocate DEET, picardin or icaridin (_sec_-butyl 2-(2-hydroxyethyl)piperidine-1-carboxylate), IR3535 (ethyl 3-(\(N\)-butylacetamido)propanoate), PMD (\(p\)-methanane-3,8-diol), AI3-35765 (1-(3-cyclohexen-1-ylcarbonyl)piperidine), and AI3-37220 (1-(3-cyclohexen-1-ylcarbonyl)-2-methylpiperidine) to class 1. However, it is obvious that the results obtained with the ML1 and MH1 models are not enough to correctly evaluate the potential influence of the log \(P\) calculation methods on the prediction of the repellent activity of chemicals. Indeed, while the results obtained with the ML1 and MH1 models allow us to validate the architectures of the TLP models and their good prediction performances, repetitions are required to statistically secure the results obtained with both models. Consequently, 92 different training sets and independent test sets of 80%/20% were randomly constituted and used to compute (MlogP or MHlogP + 16)/7/2 TLP models with the BFGS training algorithm, the entropy as the error function, and tanh and softmax as hidden and output activation functions, respectively.