## 3 Results
The MlogP and MHlogP values of chemical #537 are equal to 2.95 and 2.99, respectively (Table S2). Both values have a low reliability according to VEGA 1.1.4. In contrast, Tables S1 and S2 show that chemical #536 (capraldehyde diethyl acetal) is always correctly predicted as a non-repellent whatever the model category and its belonging to the training and test sets while its MlogP value is 4.44 (low reliability) and its MHlogP value is 5.13 (moderate reliability). Chemical #462 (1-_n_-butyl-1,2,3,4-tetrahydro-2-naphth) (on the right of chemical #537 in Figure 2) is a non-repellent correctly classified by the ML and MH models in 69.23% and 35.24% of cases as a training set member and in 19.35% and 13.33% of cases as a test set member, respectively (Table S2). The chemical belongs to the training set of the MH1 and ML1 models and it is incorrectly modeled by them (Table S1). In contrast, 100% of good predictions are obtained with the MH, MH1, ML, and ML1 models in the prediction of the activities of repellents #1163 (1-ethyl-1,2,3,4-tetrahydro-2-naphth) and #1583 (1-methyl-1,2,3,4-tetrahydro-2-naphth), whatever their location in the training and test sets (Table S2). It is interesting to note that the differences between the MlogP and MHlogP values of chemicals #462, #1163, and #1583 equal -0.66, -0.21, and 0, respectively (Table S2). The non-repellents #1563 (2-methyl-5-phenyl-2-propyl-1,3-dioxolan-4-one) and #1949 (iso-propyl benzyl ketone), located in the top middle part of Figure 2, are also more correctly classified by the TLPs including MlogP as an input neuron than those with MHlogP. Indeed, the former compound is correctly assigned to class 0 in 84.17% and 44.07% of cases when it belongs to the training sets of the ML and MH models, respectively (Table S2). As a test set member, the difference is higher, namely 93.33% and 35.29%, respectively (Table S2). Regarding chemical #1949, 95.1% versus 50.96% of good predictions were obtained as a training set member and 75.76% versus 67.74% as a test set member with the ML and MH models, respectively (Table S2). It is noteworthy that both chemicals have their activity correctly predicted only by the ML1 model. However, the differences between the MlogP and MHlogP values of each chemical are not important (Table S2). The same phenomenon showing significantly better results obtained with the ML models than the MH models can also be observed with repellents. Thus, chemical #1259 (\(o\)-_n_-hexyloxybenzaldehyde), located below compound #1949 in Figure 2, is a repellent often predicted as such in 88.78% of cases by the ML models when it belongs to the training sets, while it is allocated to class 1 in only 55.88% of cases by the MH models. The molecule is detected as a repellent in 59.46% and 48.48% of cases when it belongs to the test sets of the ML and MH models, respectively (Table S2). It is correctly allocated to class 1 by the ML1 and MH1 models as a test set member (Table S1). Interestingly, there is a large difference between the MlogP and MHlogP values that are, respectively, equal to 2.69 and 4.25 (Table S2), with a low reliability regarding the former value and moderate for the latter. Repellent #1465 (4-methoxyvalerophenone), located just below chemical #462 in Figure 2, is predicted as such in 66.97% and 34.58% of cases when it belongs to the training sets of the ML and MH models, respectively. As a test set member, only 3.85% and 0% of correct allocations to class 1 are obtained, respectively (Table S2). The chemical belongs to the training set of the ML1 and MH1 models and it is only correctly predicted by the former model (Table S1). Interestingly, the MlogP and MHlogP values equal 2.41 and 3.23 (Table S2), with a good reliability for the former calculated log \(P\) value and low for the latter. The situation is broadly the same regarding repellent #1618 (5-nonanone oxime), located near chemical #1465 in Figure 2. The ML models correctly allocate this repellent to class 1 in 64.10% of cases and the MH models in 32.26% of cases when it belongs to the training sets.