## 3 Results
Lastly, the input layer included nine descriptors accounting for the presence of specific atoms, functional groups, and structural features in the molecules. These descriptors were the number of aliphatic rings (NumAliphaticRings), the number of aromatic rings (NumAromaticRings), the number of aliphatic hydroxyl groups (fr_Al_OH), the number of carbonyl O (fr_C_O), number of aryl methyl sites for hydroxylation (fr_aryl_methyl), the number of esters (fr_ester), the number of ether oxygens, including phenoxy (fr_ether), the number of ketones (fr_ketone), and lastly, the number of methoxy groups (fr_methoxy). It is worth noting that MolWt, BalabanJ, BertzCT, TPSA, HallKierAlpha, fr_Al_OH, fr_C_O, fr_ester, fr_ether, fr_ketone, and fr_methoxy also belonged to the input layer of our previously selected 20/6/2 TLP model [25]. Among the different 17/7/2 TLPs, the ML1 and MH1 models were selected as representative of the TLPs computed with MlogP or MHlogP descriptors, respectively. Convergence was obtained in 122 cycles with ML1 and in 89 cycles with MH1. The performance criterion values calculated for the training, test, and whole sets of molecules of the selected ML1 and MH1 models are given in Tables 1 and 2, respectively. The calculated activity values obtained with both models are listed in Table S1. Inspection of Tables 1 and 2 shows that both models allow us to obtain about 92% and 90% of good predictions on the training and test sets, respectively. No overfitting can be suspected. The Matthews correlation coefficient (MCC) is widely used as a measure of the quality of binary classifications. It lies on a scale from \(-\)1.0 to 1.0, reached in case of perfect misclassification and perfect classification, respectively, while MCC = 0 corresponds to a random prediction [41]. Tables 1 and 2 reveal that the MCC values of the ML1 and MH1 models equal 0.733 and 0.742, respectively. These values and those obtained on the corresponding training and test sets are considered to be fully acceptable for real-world data [42; 43; 44; 45; 46]. The F1 scores are slightly better than the MCC values (Tables 1 and 2). F1 ranges in the interval [0, 1], where the minimum is reached when all the positive samples are misclassified, and the maximum is obtained in case of perfect classification [47]. The very high Area Under the Curve (AUC) values (Tables 1 and 2) reveal the ability of the ML1 and MH1 models to make the distinction between the two classes of activity. In addition, the G-mean and dominance values clearly reveal that both models cope well with the studied unbalanced dataset. Indeed, the Geometric Mean (G-Mean) measures the balance between classification performances on both classes of different sizes. G-mean is the square root of the product of sensitivity (true positive rate) and specificity (true negative rate). A low G-Mean value provides an indication of poor performance in the binary classification of the positive cases, even if the negative cases are correctly classified as such [48; 49]. The dominance varies from \(-1\) to \(1\). The closer the value is to one of the bounds, the greater the accuracy of the class detrimental to the other one. As a result, the closer the dominance is to \(0\), the better the model is able to correctly predict both unbalanced classes of activities [48; 50]. With G-mean values equal to about \(0.9\) and dominance values near \(0\), the ML1 and MH1 models appear to be able to equally predict the repellents and non-repellents. In other words, the unbalanced dataset does not influence the prediction performances of the selected models.