## 4 Discussion  
The 1-octanol/water partition coefficient (log \(P\)) of the organic molecules is undoubtedly the most important physicochemical property for explaining numerous of their pharmacological and toxicological effects as well as their behavior in the environment. This explains why log \(P\) is a long-established molecular descriptor in SAR and QSAR modeling, leading to thousands of published models in which it is used alone or with other molecular descriptors [17; 18; 53]. The number of experimental log \(P\) values available being very small compared to the universe of molecular structures that exist and can be drawn and/or synthetized, various methods have been proposed to reduce the time and cost of obtaining this physicochemical parameter. Among them, the QSPR models for log \(P\) occupy a prominent place because the balance of their advantages far outweighs that of their disadvantages. As a result, there exist numerous QSPR models for log \(P\) constructed from datasets of various sizes and molecular diversity, with different molecular descriptors and built from diverse statistical methods. This leads to models with different application domains. These QSPR models can be open-source/access or commercial software with advantages and disadvantages inherent to their origin. Among them, the possibility or not to estimate whether the predictions made with them belong to their applicability domain is of crucial importance. In a previous study [25], we showed that log \(P\) calculated according to the regression equation of [PERSON] et al. [28; 29] (MlogP) was a key descriptor for modeling the repellent activity of structurally diverse chemicals against _Ae. aegypti_. Consequently, it was tempted to perform a similar study with MHlogP based on the atom/fragment contribution method of [PERSON] and [PERSON] [30] to evaluate their respective influence on the prediction of repellent activity of molecules. Based on two different methodologies, freely available, well documented, and with criteria to assess the reliability of their predictions, the MlogP and MHlogP models were perfectly suited to the objectives of this study. Thus, our results clearly reveal that the MlogP and MHlogP models can behave differently according to the structure and activity of the molecules. Table S2 shows that about 50% of the molecules are always well allocated to class 0 or 1 by both categories of models regardless of their belonging to the training and test sets. It is among the non-repellents that the highest differences between the MlogP and MHlogP values are found, without impacting the prediction results. Thus, for example, 100% of good predictions are always obtained for the non-repellent #1392 (malic acid, dioleyl ester), while the MlogP and MHlogP values of this molecule are equal to 8.10 and 15.13, respectively (Table S2). Nevertheless, Figures 2 and 3 and Table S2 show that TLP models that only vary by one input neuron, namely MlogP or MHlogP, can statistically lead to very different output results. This clearly poses the problem of the correct interpretation of the results of a SAR or QSAR model when it is classically built, because in that case, only a single QSPR model per physicochemical property of interest is always chosen in its modeling process. An attempt was made to estimate which calculated log \(P\) values best characterized repellents. To do so, only the molecules always allocated to class 1 by the ML or MH models, independently of their location as training and test members, were considered. Thus, among the 318 repellents, 79 (24.84%) are always correctly classified as such by the ML models and their corresponding MlogP values range from 1.08 to 3.12 (Table S2). In the same way, 91 repellents (28.62%) are always correctly predicted by the MH models, with MHlogP values ranging from 0.94 to 4.02 (Table S2). Within these ranges of calculated log \(P\) values, 58 molecules (73.42%) have their MlogP value between 1.62 and 2.64 and 51 (56.04%) have their MHlogP value between 1.60 and 2.63.