## 3 Results  
Although the coefficient of determination (\(r^{2}\)) between the log \(P\) values calculated with both QSPR models for the entire set of 2171 molecules equals 0.89, their choice for the comparison exercise was justified. Indeed, both QSPR models behave differently according to the levels of hydrophobicity of the chemicals that are predicted. The range of MlogP values varies from \(-2.07\) to \(8.72\), while the lowest and highest M\(\mathrm{Hlog}\) values equal \(-4.76\) and \(16.11\), respectively (Table S2). Among the 2171 molecules of the dataset, there was an experimental log \(P\) value for 155 of them in VEGA 1.1.4. A scatterplot of these 155 experimental log \(P\) values versus the corresponding MlogP and M\(\mathrm{HlogP}\) values is shown in Figure 1.  
Even if the comparison is made from a dataset of limited size, inspection of Figure 1 reveals that the more hydrophobic the molecules are, the more the MlogP and M\(\mathrm{HlogP}\) values diverge. A divergence also occurs for low log \(P\) values (Figure 1). Figure 1: Scatterplot of experimental log \(P\) values versus the corresponding calculated MlogP and M\(\mathrm{HlogP}\) values for 155 dataset molecules. The selection of the best set of molecular descriptors for discriminating the repellents from the non-repellents was made by using the Feature Selection option of the data mining module of the Statistic(tm) software version 12 (StatSoft, Paris, France) and by considering the results obtained in our recent study [25]. The descriptor selection had to respond to two constraints. First, it was necessary to find a unique set of descriptors giving the best possible results with both MlogP and MHlogP descriptors without penalizing one type of classification model compared to the other. Second, the aim of the study being to estimate the potential influence of log \(P\) calculated in two different ways on the activity predictions, it was necessary to reduce as much as possible the number of other descriptors to include as input neurons in the TLP models. This was intended to limit the number of possible co-factors that would intervene in the interpretation of the models. Conveniently, the two categories of models derived with MlogP and MHlogP descriptors were named ML and MH, respectively. On the basis of numerous trials carried out by randomly selecting different training sets and independent test sets of 80%/20% we showed that the input neurons needed to include physicochemical descriptors and topological indices as well as descriptors encoding some structural features and functional groups of molecules. Thus, TLP models with 14 to 22 input neurons (including either MlogP or MHlogP) and 5 to 8 hidden neurons were tested. The cross-entropy with tanh as the hidden activation function and softmax as the output activation function often led to much better results than the sum of squares with identity, logistic, tanh, or exponential as activation functions. Thus, on the basis of hundreds of runs, we found that the most interesting models were 17/7/2 TLPs with the cross-entropy as the error function and tanh and softmax as hidden and output activation functions, respectively. The 17 input neurons included three physicochemical properties, namely the molecular weight (MoIWt), the polar surface area of the molecule (TPSA), and the 1-octanol/water partition coefficient MlogP or MHlogP. There were five topological indices [36] viz. the Balaban J index (BalabanJ) [37], which is suited to accounting for small structural changes between similar structures, the Bertz index (BertzCT) [38], which quantifies the complexity of molecules, the molecular connectivity index of first order of valence (Chi1v) [39], which encodes mostly the size of the molecules but also their degree of branching, and the \(\alpha\) value (HallKierAlpha) and the Kappa index of order 3 (Kappa3) [36, 40], which describe the shape and flexibility of the molecules.