The geometry, rotational barrier, vibrational frequency, and electrical characteristics of the compounds are precisely and reliably determined via density functional theory (DFT) [39][40]. Table 1 displays the optimized structure for each of the six distinct compounds. The computations were performed via the 6-31G basis set and the hybrid B3LYP functional technique. One of the most widely used and effective functionals for drug design is B3LYP, which can balance calculation efficiency and accuracy. The geometry, vibrational frequencies, dipole moments, and reaction energies of many pharmacological compounds can be replicated by B3LYP. Ionization potentials, electron affinities, spin densities, and NMR chemical shifts are only a few of the electrical and magnetic characteristics of pharmacological compounds that B3LYP can record. In quantum chemistry, B3LYP is regarded as a workhorse functional and has been frequently used. Because it strikes a compromise between accuracy and computing expense, it is very popular. Its ability to strike a compromise between computing cost and precision accounts for its appeal. Because it yields reasonable results over a wide range of systems, including organic and inorganic molecules, researchers frequently choose it. Frontier molecular orbitals (FMOs) provide accurate qualitative evidence of electron susceptibility and control how drugs interact with their receptors. An electron acceptor is the LUMO, which is the lowest energy orbital with an opening for electrons [41].

Table 1. Optimization of mannopyranoside (1) and its derivatives (2-7).

Table 2.Thermodynamic properties of methyl α-D-mannopyranoside (1) and its derivatives (2-7).

Normal changes in molecular structure substantially affect structural features such as thermal and molecular orbital parameters. Free energy and enthalpy data can be used to deduce the spontaneous nature of a reaction and the stability of a product [42]. For thermal stability, values that are extremely negative are better. The dipole moment also affects the development of hydrogen bonds and nonbonded interactions in drug design. The binding property may improve with increasing dipole moment. The free energy of methyl α-D-mannopyranoside is −722.276 hartrees, where the Gibbs free energies of derivatives 6 and 7 are −2828.835 and −2461.198 hartrees, respectively. The highest electronic energy was observed for 6 (−2830.171 Hartree), and the highest dipole moment was observed for 3 (7.574 Debye); these values are listed in Table 2. The presence of a bulky acylating group suggests that the possible improvement in the polarizability of derivative 6 results in the highest polarizability value of 589.445 a.u.

The quantum chemical characteristics of molecules can be reliably predicted via density functional theory (DFT) [43]. Quantum chemical parameters are essential descriptors that shed light on the electronic structure, stability, and reactivity of chemical compounds. They are obtained from density functional theory (DFT) and molecular orbital theory. While their energy gap indicates chemical stability, frontier molecular orbital energies, such as the lowest unoccupied molecular orbital (LUMO) and the highest occupied molecular orbital (HOMO), indicate a molecule’s capacity to accept and donate electrons, respectively. To predict the reactive nature of molecules in a variety of chemical and biological environments, global reactivity descriptors such as ionization potential, electron affinity, chemical hardness, softness, electronegativity, chemical potential, and electrophilicity indices can be computed from these orbital values [44][45]. Furthermore, characteristics such as the atomic charge distribution, dipole moment, polarizability, and MEP reveal details about potential sites for electrophilic and nucleophilic attack as well as intermolecular interactions. To further understand thermodynamic stability, one might consider energetic quantities such as total energy, free energy, and binding energy. Researchers can describe and predict molecular behavior at the atomic level by combining these quantum chemical characteristics, which makes them effective tools in computational chemistry, drug design, and materials science [46].

The electron density of the lowest energy orbital that is unoccupied is represented by the LUMO, and the electron density of the highest energy orbital that is fully occupied is represented by the HOMO. A crucial factor in determining a molecule’s stability and reactivity is the HOMO-LUMO gap, which is the energy differential between the HOMO and LUMO. Some new descriptors based on quantum chemistry have been computed because of the capacity of HF and DFT approaches to characterize the chemical and physical characteristics of molecules [47]. Several quantum chemical characteristics, including electronegativity (χ), chemical hardness (η), global softness (S), chemical potential (Pi), and global electrophilicity (ω), were computed from the values derived from E_LUMO (IP) and E_HOMO (EA) [48][49]. Gaussian09 was thus used to generate the molecular HOMOs and LUMOs. The HOMO-LUMO gap (ΔE = E_LUMO − E_HOMO), HOMO energy (E_HOMO), and LUMO energy (E_LUMO) values of methyl α-D-mannopyranoside (1) and its derivatives (2-7) are listed in Table 3.

Table 3.Data on the chemical reactivity descriptor of methyl α-D-mannopyranoside (1) and its derivatives (2-7).

Table 3 presents the values of orbital energies, along with the two global chemical descriptors, hardness and softness, which are also calculated for all the compounds. The highest softness was observed for compound 7. Compound 7 also had the lowest HOMO‒LUMO gap and hardness, indicating that the molecule is more reactive than the other compounds, according to [50] (Figure 2).

With an energy gap of 7.407 eV, compound 1 had the highest chemical hardness (3.703 eV) and the lowest softness (0.270 eV). The lowest energy gap, 5.304 eV, is found in compound 7 (4-t-butylbenzoyl), which also has the highest softness (0.377 eV) and the lowest hardness (2.652 eV) (Table 3 and Figure 2) [51]. These results clarify the relative stability and reactivity of these chemicals, which will facilitate further research and design.

Figure2.Molecular orbital distribution plots of the HOMO and LUMO.

The molecular electrostatic potential (MEP) is widely used as a reactivity map to display the most likely region for the electrophilic and nucleophilic attack of charged points, such as reagents, on organic molecules [52]. This method helps to interpret the biological recognition process and hydrogen bonding interactions. The MEP counter map provides a simple way to predict how different geometries could interact. The MEPs of methyl α-D-mannopyranoside and its derivatives (2 to 7) were obtained on the basis of the DFT model with the basis set 6-31G optimized result, as shown in Figure 3. The importance of the MEP lies in the fact that it simultaneously displays molecular size and shape, as well as positive, negative, and neutral electrostatic potential regions in terms of color grading, and is very useful in research on the relationships between molecular structure and physicochemical properties. The molecular electrostatic potential (MEP) was calculated to predict the reactive sites for electrophilic and nucleophilic attack of the optimized structure of methyl α-D-mannopyranoside and its derivatives (2 to 7). The different values of the electrostatic potential are represented by different colors.

Figure 3.Molecular electrostatic potential map of methyl α-D-mannopyranoside (1) and its derivatives (2 to 7).

The density of states (DOS), which offers a straightforward method for characterizing intricate electronic structures, is arguably the most crucial idea for comprehending the physical characteristics of materials. The DOS provides important visible features that underpin a material’s electrical and optical characteristics, such as the band gap and carrier effective masses [53]. The HOMO of compound 6 is located between −6 and −7 eV. The LUMO can be observed close to 0 eV, and the HOMO of compound 7 is the same as that of compound 6, but the LUMO is close to −2 eV (Figure 4). Nevertheless, the density of states profiles of the derivatives exhibits minor variations because of their distinct structural attributes [54].

Figure 4.DOS plot representing the HOMO‒LUMO gap of compounds 6 and 7.

NBO analysis is a quantum chemical tool that helps us understand molecules in terms of localized orbitals that are close to classical chemical concepts such as bonds, lone pairs, and hybridization. The investigation of the influences of covalence and hybridization in polyatomic wave functions resulted in the creation of the natural bond orbital (NBO) analysis technique. The work of Foster and Winhold [55] was expanded upon by Reed et al.[56], who utilized NBO analysis, which revealed mainly H-bonded and other strongly bound van der Waals complexes. The results for compounds 2 and 7 are presented in Figure 5 and Figure 6, respectively.

Figure 5.Comparison of the NBO charge and Mulliken charge of compound 2.

Figure 6. Comparison of the NBO charge and the Mulliken charge of compound 7.

A popular spectroscopic method for analyzing the structure of carbohydrates is infrared (IR) spectroscopy, which is quick, nondestructive, and easily accessible [57]. The FTIR spectrum of compound 2 exhibited strong ester carbonyl absorption at 1740 cm⁻¹ along with a broad −OH stretching band at 3400 cm⁻¹, indicating partial esterification at the C-6 position, whereas characteristic C−O stretching vibrations were observed in the 1050 - 1150 cm⁻¹ region. The FTIR spectrum of 7 displayed multiple strong ester carbonyl absorptions between 1710 and 1740 cm⁻¹, along with aromatic C=C stretching bands at 1600 and 1500 cm⁻¹, para-substituted aromatic out-of-plane bending near 830 cm⁻¹, and intense C−O stretching in the 1100 - 1200 cm⁻¹ region, confirming complete esterification at positions C-2, C-3, C-4, and C-6. In Figure 7, compound 2 shows a broad −OH peak at 3480 cm⁻¹ and a sharp peak at 1717 cm⁻¹. However, after acylation, the −OH group should disappear because the acylating agent blocks it, but the C=O peak should remain.

Figure 7. IR spectra of compounds 2 (right) and 7 (left).

The PreADMET server was used to examine whether the modified chemicals resulted in any toxicity or changed the pharmacokinetic profile. pkCSM is an online tool for in silico drug-like library construction and ADME data prediction. A number of pharmacokinetic and pharmacodynamic factors, including acute toxicity in rats, human intestinal absorption, the blood-brain barrier, cytochrome P450 inhibition, and human ether-a-go-go-related gene inhibition, have been considered. Table 4 provides a summary of the findings. The pharmacokinetic and toxicological characteristics of the synthesized carbohydrate derivatives were assessed utilizing the pkCSM predictive model, which revealed that structural modifications throughout the series systematically influence absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The values associated with human intestinal absorption (HIA) exhibited significant structural dependence, increasing dramatically from a modest 38.567% in compound 1 and 43.201% in compound 2 to an exceptional peak of 95.804% in compound 7, indicating that the sequential incorporation of lipophilic acyl protecting groups enhances membrane permeability. Moreover, all the compounds exhibited negative blood-brain barrier (BBB) permeability values (logBB = −0.851 to −2.318), thereby meeting the essential safety criteria for the mitigation of central nervous system (CNS) side effects. Although the more hydrophilic, low-absorption structures (Entries 1 and 2) do not function as P-glycoprotein inhibitors, the highly permeable lipid-rich variants (compounds 3-7) act as P-glycoprotein inhibitors, suggesting their potential to modulate efflux pump kinetics. The solubility in pure water (logS, mol/L) progressively decreased from −0.645 (compound 1) to −3.325 (compound 7) concomitant with increases in molecular weight and lipophilicity, aligning with the trends observed in membrane tracking studies. From a metabolic and safety perspective, none of the investigated derivatives serve as inhibitors of the cytochrome P450 isoform CYP2C19, nor do they possess cardiotoxic effects, as corroborated by a consistent “No” for hERG channel inhibition [58]. Ultimately, toxicity profiling indicated moderate threshold doses for rat carcinogenicity (1.474 to 2.653 mg/kg/day) accompanied by a highly favorable safety outlook, thereby reinforcing the therapeutic potential of these mannopyranoside derivatives as non-CNS-active candidate scaffolds. These findings suggest that the compounds are excellent peripherally selective D2 inhibitors. By preferentially targeting peripheral dopaminergic sites—such as the gastrointestinal tract or systemic vasculature—without crossing the blood–brain barrier, these mannopyranoside derivatives offer a highly promising therapeutic avenue for treating peripheral dopaminergic disorders (e.g., gastroparesis or severe emesis) while completely avoiding central extrapyramidal or neurological side effects. Alternatively, these profiles establish a clear baseline for future lead optimization, serving as structural blueprints for introducing targeted prodrug linkages or specific de-esterification strategies designed to systematically bypass efflux pumps and tune central bioavailability.

Table 4.ADMET analysis of mannopyranoside (1) and its derivatives (1-7).

Table 5.Prediction of the biological activities of mannopyranoside (1) and its derivatives (2-7) via PASS.

A software program called PASS (Prediction of Activity Spectra for Substances) was created as a means of assessing an organic drug-like molecule’s overall biological potential. PASS uses the structure of organic molecules to predict multiple biological activity categories simultaneously. Therefore, before virtual compounds undergo chemical production and biological testing, PASS can be used to assess their biological activity profiles. Pa (probability “to be active”) calculates the likelihood that the compound under study is a member of the active compound subclass (similar to the most common molecular structures in a subset of “actives” in the PASS training set). In the leave-one-out cross-validation process, the average prediction error for the whole PASS training set is called the invariant error of prediction (IEP). The Way2Drug primary web server made the PASS predictions. PASS Online uses structural formulas to predict the biological activity of organic compounds with a 95% accuracy rate and over 4000 different categories of activity [59]. Table 5 presents the predicted biological activities of the mannopyranoside derivatives (1-7), including antibacterial, anticarcinogenic, antifungal, and anti-inflammatory effects. The table lists Pa (probability of activity) and Pi (probability of inactivity) for each derivative. Higher Pa values indicate a greater likelihood that the compound exhibits the specified activity. Most derivatives (2-7) show moderate activity (Pa ≈ 0.552 - 0.569). Compound 1 has the highest predicted anticarcinogenic potential (Pa = 0.731), whereas those of the other compounds range from 0.477 - 0.656. These findings suggest that these derivatives may help prevent carcinogenesis rather than directly treat existing cancer. Compounds 3-6 show strong antifungal potential (Pa = 0.721). The Pa values ranged from 0.596 - 0.634, indicating moderate activity across the derivatives. Overall, these derivatives possess multitargeted biological potential, with antiviral, antimicrobial, and anticarcinogenic effects, which supports their suitability for further drug development and molecular docking studies.

To ensure the accuracy, reliability, and predictive validity of the molecular docking simulations carried out in this study, a native ligand redocking protocol was executed prior to screening the synthesized methyl D-mannopyranoside derivatives. The cocrystallized antagonist, risperidone, was computationally extracted from the active binding pocket of the human dopamine receptor D2 (PDB ID: 6CM4) and subjected to an identical, unbiased redocking sequence within the designated grid coordinates. The structural reliability of the docking protocol was assessed by calculating the root-mean-square deviation (RMSD) between the experimentally determined crystal structure conformation of risperidone and its best-scoring, redocked computational pose. As illustrated by the alignment profiles, the redocked ligand configuration strongly mirrored the native spatial geometry within the binding pocket (Figure 8). The calculated RMSD was 0.0402 Å, which falls well below the generally accepted threshold of 2.0 Å for highly successful docking validation.

Figure 8. Superimposition of the co-crystallized ligand (blue) and the docked ligand (green).

A computational method for predicting ligand binding affinities to receptor proteins is called molecular docking. It can be used in nutraceutical research, but it has become a powerful tool for medication development [60]. Molecular docking has emerged as a crucial component of in silico drug development in recent years. This technique involves predicting the interaction between a small molecule and a protein at the atomic level [61]. Numerous computational tools and algorithms for molecular docking approaches are accessible, both commercially and at no cost. These programs and tools have been developed and are currently being used in drug research and academic fields [62]-[65]. The efficacy of the docking algorithms in determining the conformation of the protein-bound ligand was evaluated through redocking of the cocrystallized ligand to validate the precision of the docking methodology [66]. Protein–ligand complex docking investigations were conducted via the Lamarckian genetic algorithm (LGA) [67]. Molecular docking is a technique commonly used in molecular modeling to examine in detail the interactions between a ligand and a receptor and identify the ligand’s preferred orientation with respect to a target receptor. Comprehensive in silico molecular docking simulations were meticulously executed on the synthesized methyl α-D-mannopyranoside derivatives in relation to the high-resolution structural conformation of the human dopamine D2 receptor (DRD2, PDB ID: 6CM4). The crystal structure was taken from the Protein Data Bank. All of the molecules were successfully docked within the receptor active site, as confirmed by the common interacting residues [68][69]. The studied receptor was prepared by removing any nonprotein components or water molecules prior to starting the molecular docking calculation. The binding affinities of the derivatives (1-7) for 6CM4 ranged from approximately −5.2 to −9.7 kcal mol-1 (Table 6 and Figure 9).

Table6.The binding affinity (kcal/mol) and nonbonding interactions of methyl α-D-mannopyranoside (1) and its derivatives (2-7) and native 8NU with 6CM4 were evaluated.

H = Conventional hydrogen bond; C = Carbon–hydrogen bond; A = alkyl; PA = Pi-alkyl; PPS = Pi–Pi stacked; PS = Pi–Sigma; PPT = Pi–Pi T-shaped.

The docking methodology was effectively validated through the redocking of the co-crystallized atypical antipsychotic ligand 8NU2 (Risperidone), which successfully reproduced its native binding conformation with a remarkable affinity of −11.9 kcal/mol. This validation procedure accurately mirrored the experimental structural fingerprint, securing anchorage via a pivotal fluorine-mediated halogen interaction with CYS118 (3.30 Å), a robust conventional hydrogen bond with TYR416 (2.04 Å), and a four-way T-shaped π-π stacking nesting matrix involving TRP100, TRP386, and PHE390. After the protocol was validated, the binding scores of the screened carbohydrate derivatives ranged from −5.2 kcal/mol to −9.7 kcal/mol. The configuration with the lowest energy was identified in compound 1 (−5.2 kcal/mol), which adopted a shallow, polar orientation primarily interacting with SER193 and SER197. In contrast, the intermediate conformations (compounds 3, 4, and 6) adapted to the receptor cavity by embedding deeply into the lipophilic and aromatic ceiling of the pocket, thereby establishing extensive nonpolar contact networks across the residues TRP100, VAL91, LEU94, PHE110, PHE189, and TRP413. Notably, the structural assessment addressed significant peer-review inquiries pertaining to target validation by capturing the canonical aminergic anchoring linkage across multiple poses. Compounds 5 (−6.3 kcal/mol) and 7 (−9.7 kcal/mol) both successfully formed a direct carbon–hydrogen bond with the carboxylate oxygen of the highly conserved ASP114 (3.32 Å) residue. In addition, nonbonding 3D interactions and 2D interactions of (A) compound 7 with 6CM4 and (B) 8NU with 6CM4 are shown in Figure 10. Compound 7 emerged as the most energetically favorable lead compound throughout the entire docking series and was uniquely stabilized by a compact network of hydrogen and hydrophobic interactions (Table 6 and Figure 11). These findings furnish compelling thermodynamic and structural evidence that the stereochemically rich hydroxyl core of the mannopyranoside scaffold, in conjunction with hydrophobic acyl groups, fulfills the essential pharmacophoric criteria required to serve as a viable lead series for novel dopamine D2 receptor inhibitors.

Figure 9. Docked poses of compound 7 (blue) and the native ligand (cyan) at the inhibition binding site of 6CM4.

Figure 10.Nonbonding 3D interactions and 2D interactions of (A) compound 7 with 6CM4 and (B) 8NU with 6CM4.

Figure 11.Hydrogen bonds and hydrophobicity of compound 7 with 6CM4.