## 3 Results
Each model was selected on the basis of its prediction performances from batches of 500 to 1000 models obtained under the same conditions. To secure the prediction diversity of the chemicals in both activity classes, sometimes more than one modeling result was selected. This led to the constitution of a data matrix of 135 modeling results for each category of models, namely those including MlogP or MHlogP among the input neurons (Table S2). Among the 135 different ML models including the MlogP descriptor, compound #430 (\(N\)-butyl imide 1,2-dicarboxy-3,6-endomethylene-4 cyclohexene) was only allocated twice to a test set, having the lowest occurrence as a test set member. At the opposite end, 2(2(2,4,5,6-tetrachlorophenoxyoxy)ethoxy) ethyl chloride (#2041) was allocated 52 times to a test set and it is the compound with the highest occurrence. Compound #430 was also found with the lowest occurrence as a test set member of the 135 MH models including the MHlogP descriptor, but it was allocated seven times to a test set. On the other hand, chloroacetic acid, cyclohexyl ester (#620), was found 54 times as a test set member (Table S2). To better highlight the differences between both sets of 135 modeling results, the percentages of good predictions (GPs) obtained with the ML versus MH models for the training and test sets are represented in Figure 2 and Figure 3, respectively. Figure 3: Percentages of good predictions (%GPs) obtained with the MH versus ML models for the test sets. Figure 2: Percentages of good predictions (%GPs) obtained with the MH versus ML models for the training sets. There is an important concentration of points in the upper right corner of Figure 2, revealing that many non-repellents (in grey) and repellents (in red) are always correctly allocated to their activity class by the two categories of models regardless of the composition of the training and test sets. In a more precise way, inspection of Table S2 shows that 66 repellents (20.75%) and 967 non-repellents (52.19%) are always correctly predicted by both the ML and MH models, whatever their belonging to the training and test sets. Among the former group of repellents, ten (15.15%) show a difference between their MlogP and MHlogP values \(\geq\) 0.7 (absolute value, av), and among them, six have a difference \(\geq\) 1 (av) (Table S2). Thus, chemicals #753, #754, #1288, #1289, #1293, and #1298 are repellents with differences between their MlogP and MHlogP values ranging from 1 to 1.22 (av). In the same way, there are also repellents that are always allocated to class 1 by the ML and MH models and which show no significant differences between their MlogP and MHlogP values. It is the case, for example, of chemicals #913, #1026, #1450, #1451, #1487, #1499, and #1583, which present a difference between their MlogP and MHlogP values \(<\) 0.1 (av). Among the 967 non-repellents always allocated to class 0 by the ML and MH models, 650 (67.22%) show a difference between their MlogP and MHlogP values \(\geq\) 0.7 (av) and 513 (53.05%) present a difference \(\geq\) 1 (av). The highest difference is found with the malic acid, dioleyl ester (#1392), having MlogP and MHlogP values equal to 8.1 and 15.13, respectively (Table S2). In contrast, there are also non-repellents with similar MlogP and MHlogP values. It is the case, for example, of compounds #256, #836, #1861, and #1914, for which the differences between their MlogP and MHlogP values are \(\leq\) 0.03 (av). Inspection of the lower left corner of Figure 2 shows that the number of molecules having their repellent activity very poorly predicted by the ML and MH models is low. These two poles exist for the test set molecules (Figure 3). However, more often, we observe different behaviors, as shown in the top right corner of Figure 3, with molecules whose repellent activity is always well predicted by one type of model, while for the other one, it can be as low as 60% or even less.