4.1 QSAR analysis
The considered model was carefully chosen and reported because it is statistically fit with the following assessment parameters as compared to other constructed model: R2 of 0.937, \(R_{{{\text{adj}}}}^{2}\) of 0.863, \(Q_{{{\text{cv}}}}^{2}\) of 0.788, \(R_{{{\text{test}}}}^{{2}}\) of 0.756, LOF of 0.0268, and \(cR_{{\text{p}}}^{2}\) of 0.677. The selected model was found to have passed the minimum recommended values for validation of good QSAR models as reported in [24]. The details of the descriptors used in the model are listed in Table 2.
4.2 Mechanistic information of descriptors in the model built
Table 2 provides a comprehensive description of the molecular descriptors of the constructed models. Furthermore, the model showed a positive contribution from the descriptors AATS7i, MATS5p, and SpMin7_Bhe, but a negative contribution from the descriptor GATS6c. This means that an increase in the magnitude of AATS7i, MATS5p, and SpMin7_Bhe descriptors will positively influence the prediction of PIC50 with the negative influence of the GATS6c descriptor. However, the AATS7i descriptor has the highest contribution, and is the most significant descriptor to be considered in the design of new hypothetical compounds. In addition, the signs of the regression coefficients for each descriptor indicated the direction of influence of the descriptors in the models, such that a positive regression coefficient associated with a descriptor will augment the activity profile of a compound, while the negative coefficient will diminish the activity of the compound.
4.3 Derivation of the model and models and validation
The influence of each descriptor in the constructed model was assessed by determining standard regression coefficients Xj and AE [24]. Table 3 shows the strength and direction, as well as size and symbols for the Xj and AE values with which each descriptor influences the activity model. The connection between the descriptors and the activity of each compound was determined using ANOVA. The probability value for each descriptor at the 95% confidence level was found to be p \(< \frac{1}{20}\) 1, as shown in Table 3. Several statistical investigations were conducted on the calculated molecular properties in order to assess their validity.
The VIF was evaluated to define the extent of correlation between each descriptor. Generally, a VIF equal to 1 ≥ 5 signifies the non-existence of inter-correlations present in each of descriptor. However, VIF ≥ 10 implies that the developed model is unsteady [25]. The VIF for each descriptor in the built model, which was found to be less than 5, as reported in Table 3, affirms that the descriptors were meaningfully orthogonal to each order because there was no inter-correlation between them. Therefore, the alternative hypothesis is accepted. This implies a direct connection between the biological activity of each compound and the descriptors that influence the built model.
The molecular properties are highlighted in Table 4 with correlation coefficients of < ± 0.6 correlation coefficient between them, which indicates that all properties were annulled of multicollinearity.
Table 5 shows the validity results of the internal assessment to guarantee that the model is reliable. The results confirmed the stability and robustness of the model as valid because the calculated parameters were all in full agreement with general validation criteria (Table 5).
Figures 2 and 3 show the graphs of calculated activity versus observed activity of a training set and a graph of calculated activity versus observed activity of a test set, respectively. It can be observed that the values of the test sets are in close agreement with the training set values.
William’s graph shows the LAD, as shown in Fig. 4. The leverage values of compounds 10 and 15 were observed to be higher than h* = 0.75 (is, warning leverage). Thus, it can be inferred that compounds 10 and 15 are influential molecules. Moreover, it was also observed that all the compounds were within the defined space of 2.5, which indicates that no compound is said to be an outlier.
4.4 Molecular docking studies
The results of the binding energy of the selected benzimidazolinone derivatives (compounds 13 and 21) and the reference drug to a vital AChE implicated in the pathogenesis of AD from the molecular docking study are, respectively, shown in Figs. 5, 6, and 7, with the values for compounds 13 and 21 having a higher binding affinity for AChE than the approved drug. In order to study the binding mode and selectivity of most active compounds 34 and 38 with AChE (PDB code: 4MOE), Compound 13 was docked into the active site of the AChE domain protein with a splendid binding score of − 11.2 kcal/mol involving the following interactions: conventional hydrogen bonding with Val281, Phe75, Met164, and carbon hydrogen bond–bonding, Asp76, Pro56, and Leu547Ala320, Ile77, and π-Alkyl interactions Trp168, Cys583, Pro594, His548 interactions are shown with their 3D interactions and surface interactions in Fig. 5. Compound 21 was docked into the active site of the AChE domain protein with an excellent binding score of 10.9 kcal/mol involving the following interactions: conventional hydrogen bonding with Asp211, Pro248, Lys411, and carbon hydrogen bonds Arg245, Pro431, Asp430, and π-σ bonding Arg245 and π-Alkyl interactions Arg245, Pro427, interactions shown with its 3D interactions and surface interactions are shown in Fig. 6. The reference compound docked into the active site of the AChE domain protein with an excellent binding score of 9.4 kcal/mol, involving the following interactions: Van der Waals bond Ser216, Carbon Hydrogen bond Thr208, Arg245, Glm207, Pi Cation interaction Lys411,—interaction Thr244, Amide-bonding Glu215 and π-Alkyl interactions are shown with their 3D interactions and surface interactions in Fig. 7.
4.5 Drug change assessment of selected compounds
To supplement the findings of the 3D-QSAR and docking studies, we conducted in silico ADMET analyses of the two molecules listed in Tables 9 and 10. The ability to reach targets in bioactive form was assessed using the http://swissadme.ch and http://biosig.unimelb.edu.au/pkcsm/web platforms. The technologies of these webs employ a reasonable degree of certainty, as false-positive results are common in biochemical assays for small molecules [26].
Table 9 indicates that the absorption characteristics of compounds 13 and 21 are capable of oral availability owing to optimal cell permeability. Human intestinal absorption and skin permeability were > 0.8, > 80% and < −2.5, respectively, are shown in Table 9.
The volume of distribution of the studied compounds was > 0.45 which indicates that the drug is distributed in the plasma and describes the extent of drug distribution. Together with the unbound fraction, which labels the portion of free drug in plasma that may extravasate, these are two of the most significant pharmacokinetic medication parameters. These two parameters have an adequate plasma distribution profile, with a fraction of the unbound drug between 0 ≥ 0.157. These values show that the molecules can circulate well and present an important unbound fraction in the plasma, thus becoming available to interact with the pharmacological target. The two compounds can penetrate the central nervous system owing to the volume of distribution and fraction unbound as indicated in Table 9.
As shown in Table 9, cytochrome P450 (CYP) molecules are indispensable information sources, this superfamily of isoenzymes is key players [27]. The synergy between CYP and P-gp can process small molecules to improve the protection of tissues and organisms [28]. Estimation of therapeutic molecules that are major isoform substrates (CYP2D6, CYP3A4, and CYP1A2) [29, 30]. Therefore, compounds 13 and 21 were brilliant CYP substrates.
Finally, from Table 9, the expected values of the total clearance, which measures the efficiency of the body in eliminating a drug, indicate that the two compounds have a noble renal elimination and are not substrates of the renal organic cation transporter 2 (OCT2). In conclusion, the compounds passed the AMES and Minnow toxicity tests, and did not present any particular toxicity problems. The overall analysis of Table 9 highlights that compounds 13 and 21 could be outstanding candidates as drugs, or could lead to further studies and manipulations.
Table 10 displays other drug-likeness rules, such as Ghose, Veber, Egan, and Muegge violations, and all are satisfied by the two molecules that will provide the lead like rule with high affinity in high-throughput screens that allow for the discovery and exploitation of additional interactions in the lead-optimization phase [31,32,33,34]. Furthermore, the PAINS model, which was designed to exclude small molecules that are likely to produce false positives in biological assays, paid no attention to compounds 13 and 21.
Table 11 lists the physicochemical properties of compounds 13 and 21. The compounds passed Lipinski’s rule of five [35]. This further demonstrates the druggability of these compounds.
The predicted bioactivity scores of the selected compounds obtained using the Molinspiration software v2018.03 Chemoinformatics tools are given in Table 12. For the bioactivity score, the G protein coupled receptor (GPCR) ligand was active for both compounds, with value of 0.32 and 0.17. In [36], modulators of ion channels permit charged particles across cell membranes, and they are an important receptor in the healing tract. These two compounds were found to be active. All the compounds were moderately active as kinase inhibitors. In a previous study [37]. Nuclear receptors play a combinatorial role in inflammation and immunity. The two compounds were active, but compound 13 was more active in terms of protease inhibitor bioactivity scores. The two compounds were moderately active. The values for enzyme inhibition showed that all compounds were highly active. The activity score profile of the compounds showed that they were biologically active and have physiological influence.
Figure 8 illustrates the oral bioavailability graph of the two compounds on the basis of the six features discussed in physicochemical properties. The compounds have shown results within these limits, and these two compounds have good physio-chemical profile, a necessary parameter in drugs or clinical trials. The six physicochemical properties are lipophilicity, size, polarity, solubility, flexibility, and saturation. Descriptors were used on each axis to define physicochemical range [38, 39]. The pink region was the drug-like consideration of the molecule in the radar graph. The compounds obey Lipinski’s rule of five. Therefore, the two compounds displayed values within the interval known for medicine.
Figure 9 shows points located in the BOILED-Egg’s yolk that signified the molecules predicted to passively permeate through the blood–brain barrier, whereas the egg white was relative to the molecules predicted to be passively absorbed by the gastrointestinal tract. Blue dots indicate the molecules which emanate from the central nervous system with the aid of P-glycoprotein. Overall, the plot showed that compounds 13 and 21 have excellent bioavailability.