## 3 Results
The calculated log \(P\) values have a low and moderate reliability, respectively. As a training set  
Repellent #656 (caprylic anhydride) is also characterized by high MlogP and MHlogP values (Figures 4 and 5) that are equal to 4.16 (low reliability) for the former log \(P\) descriptor and 6.57 (good reliability) for the latter (Table S2). The chemical is correctly allocated to class 1 by the ML and MH models as a training set member in only 36.94% and 25.45% of cases, respectively. As a test set member, its repellent activity is wrongly predicted by the ML1 and MH1 models (Table S1), while it is correctly allocated to class 1 in only 25% of cases by the ML models versus 8% by the MH models (Table S2). Since a significant number of non-repellents have high calculated log \(P\) values and their activity is correctly predicted by the two categories of models, it is not surprising that repellent molecules with high log \(P\) values have their activity incorrectly predicted by the ML and MH models. However, this crude rule is not so crisp. Indeed, there are repellents with average log \(P\) values that have their activity poorly predicted by the models. Thus, repellent #1880 (pivalic acid, 1,5-pentanediol diester) has its activity correctly predicted by the ML models in only 1.65% of cases when it belongs to the training sets and never as a test set member. The situation is the same with the MH models, namely 6.4% versus 0%, respectively (Table S2). Chemical #1880 is also wrongly predicted by the ML1 and MH1 models (Table S1). However, its MlogP and MHlogP values are equal to 3.11 and 4.60 (Table S2), with a low reliability for both calculated values. In the same way, chemical #1551 (4-methyl-2-pentenoic acid, ethyl ester) has MlogP and MHlogP values equal to 1.95 and 2.54 (Table S2), and the reliability of the two calculated log \(P\) values was estimated as moderate and good, respectively. However, it is only allocated to class 1 in 4.31% and 0% of cases as training and test set members of the ML models, respectively (Table S2). The situation is nearly the same with the MH models, because when belonging to the training sets, only 5.79% of correct classifications are obtained versus 0% when chemical #1551 is included in the test sets of the MH models (Table S2). Lastly, because Figure 1 shows that significant differences can exist between the highest or, on the contrary, the lowest values of MlogP and MHlogP for the compounds for which experimental log \(P\) values were available, a focus was placed on the corresponding results obtained with both categories of models for these chemicals. The large differences seem to have a limited effect on the prediction results. Thus, for example, acetylacetone (#136), cyclopropyl nitrile (#802), and \(N\),\(N\)-dimethyl formamide (#960) are non-repellents always predicted as such by the ML and MH models, whatever their belonging to the training and test sets, while their experimental log \(P\)/MlogP/MHlogP values are equal to \(0.4/0.08/-1.3\), \(0.68/0.26/-0.38\), and \(-1.01/0.13/-0.51\), respectively. In the same way, linoleic acid, methyl ester (#1380), octadecanoic acid, methyl ester (#1622) and phthal acid, dicapril ester (#1839), to cite as illustrative examples, are non-repellents always allocated to class 0 by the ML and MH models, while their experimental log \(P\)/MlogP/MHlogP values equal 6.82/4.80/7.80, 8.35/5.92/8.23, and 8.10/6.34/8.54, respectively (Table S2).