DTI Drug Target Insights 2025; 19: 71-90 ISSN 1177-3928 | DOI: 10.33393/dti.2025.3495 ORIGINAL RESEARCH ARTICLE Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti © 2025 The Authors. This article is published by AboutScience and licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Commercial use is not permitted and is subject to Publisher’s permissions. Full information is available at www.aboutscience.eu Integrated in vitro, microarray, and network pharmacology analysis reveals the multi-target anti-diabetic potential of Vigna unguiculata Haseeba Sardar1, Fatima Noor2, Syed Muhammad Mukarram Shah 3, Ashraf Ullah Khan 4, Jamelah S Al-Otaibi5, Fazal Hadi 1, Maria Daglia 6,7, Haroon Khan1,8 1Department of Pharmacy, Abdul Wali Khan University, Mardan - Pakistan 2Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore - Pakistan 3Department of Pharmacy, University of Swabi, Swabi - Pakistan 4Faculty of Pharmaceutical Sciences, Abasyn University, Peshawar - Pakistan 5Department of Chemistry, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh - Saudi Arabia 6Department of Pharmacy, University of Naples Federico II, Naples - Italy 7International Research Center for Food Nutrition and Safety, Jiangsu University, Zhenjiang - China 8Department of Pharmacy, Korea University, Sejong - South Korea ABSTRACT Introduction: Diabetes mellitus (DM), particularly type 2 DM (T2DM), is a chronic metabolic disorder requiring novel therapeutic approaches as the available therapies are not meeting the current challenges. This study inves- tigates the anti-diabetic potential of Vigna unguiculata using a network pharmacology approach, supported by in vitro and in silico analyses. Methods: The plant was collected from Khyber Pakhtunkhwa, Pakistan, and subjected to hydroalcoholic extrac- tion and fractionation. In vitro assays included α-amylase, α-glucosidase, and aldose reductase. Target prediction using STITCH and SwissTargetPrediction identified 88 common genes linked to T2DM. Protein-protein interaction (PPI) network analysis highlighted key genes like EGFR, PTGS2, and TLR4 as central nodes in diabetes-related pathways. Molecular docking was used to study the binding affinities of compounds. Results: IC50 values were determined using IBM SPSS Statistics 21 software. The data underwent analysis using one-way ANOVA followed by Dunnett’s multiple comparison test. Significance value was determined at *p < 0.05, **p < 0.01 and ***p < 0.001. In-vitro assays demonstrated significant α-amylase, α-glucosidase, and aldose reduc- tase inhibitory activities. Phytochemical screening identified several bioactive compounds. Functional annota- tion and KEGG pathway analysis confirmed these genes’ roles in crucial metabolic pathways. Virtual screening revealed strong binding affinities of compounds like Stigmasterol, Luteoline, and Quercetin with GSK3B, PTGS2, and TLR4. The Molecular Dynamics (MD) simulation, binding free energy calculations (MM-PBSA and MM-GBSA), confirmed the results of Virtual screening. Conclusion: In short, these findings underscore V. unguiculata as a promising source for anti-diabetic agents, sup- porting further clinical trials for T2DM management. Keywords: Diabetes, In vitro, Molecular docking, MD simulation, Network pharmacology, Protein-protein interac- tion (PPI) network, Vigna unguiculata eventual malfunction of pancreatic beta-cells (1). The impact of this prevalent form of diabetes is significant worldwide, accounting for around 90% of cases. This impact is felt deeply in terms of both individual health and healthcare systems (2,3). T2DM’s worldwide occurrence is on the rise due to fac- tors like longer life expectancy, lack of physical activity, and shifts in eating habits, which makes it a significant issue in the epidemiology of non-communicable diseases (4). The mortal- ity rates linked to T2DM are concerning, since it leads to car- diovascular diseases, kidney failure, lower limb amputation, and diabetic retinopathy, as well as other complications (5). The mortality rate linked to T2DM is measured in scientific research using hazard ratios or relative risks when compared to non-diabetic groups. For example, people with T2DM have Received: February 12, 2025 Accepted: June 18, 2025 Published online: August 21, 2025 This article includes supplementary material. Corresponding author: Haroon Khan email: haroonkhan@awkum.edu.pk; haroonkhan@korea.ac.kr Introduction Type 2 diabetes mellitus (T2DM) is a growing and impor- tant health issue worldwide, characterized by continuous high blood sugar levels caused by resistance to insulin and https://doi.org/10.33393/dti.2025.3495 https://creativecommons.org/licenses/by-nc/4.0/legalcode https://orcid.org/0000-0001-5412-834X https://orcid.org/0000-0003-1940-2053 https://orcid.org/0009-0004-4344-1145 https://orcid.org/0000-0002-4870-7713 mailto:haroonkhan@awkum.edu.pk mailto:haroonkhan%40korea.ac.kr?subject= In silico Anti-diabetic Potential of Vigna unguiculata72 © 2025 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti double the likelihood of dying at any age compared to those without the condition. Treatment for T2DM mainly focuses on controlling blood sugar levels using lifestyle changes, oral anti-diabetic medi- cations, and insulin treatment (6). The methods mentioned typically do not fully tackle the multifaceted characteristics of the illness. Many traditional treatments focus on only one element of the disease’s intricate pathophysiology, potentially resulting in less than ideal results and failing to stop the advancement of the disease (7). With these restric- tions in mind, there is an urgent requirement for a transition towards multi-target drug discovery. This method seeks to create medications that can concurrently impact numerous pathways associated with T2DM, providing a more compre- hensive treatment option. These approaches are expected to enhance treatment effectiveness and patient outcomes by tackling the various mechanisms involved in the development and advancement of the disease. Therefore, T2DM continues to be a significant health challenge with substantial morbid- ity and mortality. The insufficiency of current single-target treatments emphasizes the need for innovative treatment approaches, especially those involving multi-target interven- tions, to more effectively handle and potentially reverse this intricate and widespread disease. Conventional treatments primarily focus on managing blood glucose levels and mitigating symptoms, yet they often fail to address the underlying causes and multiple pathways involved in the disease’s progression. Recent advancements in network pharmacology have underscored the importance of targeting multiple biological pathways simultaneously to achieve more effective therapeutic outcomes. Network phar- macology is a cutting-edge approach that integrates systems biology, multi-target drug actions, and bioinformatics to sys- tematically explore the therapeutic potential of drugs, includ- ing those derived from medicinal plants (8,9). This approach is especially relevant in the context of T2DM, where the interplay between insulin signaling, inflammation, and oxi- dative stress contributes to the disease’s complexity. In this study, we explore the potential of V. unguiculata, a plant with a rich history in traditional medicine, as a source of multi- target anti-diabetic agents. V. unguiculata (commonly known as cowpea or lobia) is a leguminous plant belonging to the Fabaceae family (10). Traditionally used in various medicinal practices, it has been suggested to possess anti-diabetic prop- erties, potentially due to its bioactive compounds that may influence glucose metabolism and insulin sensitivity (11,12). However, the exact mechanism is currently unknown. Following that, this study provides compelling evidence for the anti-diabetic potential of V. unguiculata through an integrated approach that combines traditional knowledge with modern scientific techniques. The study highlights the importance of a multi-target therapeutic strategy, particu- larly in managing complex diseases like T2DM, where multi- ple biological processes are dysregulated. The V. unguiculata has been traditionally employed for the ailment of various diseases, including DM. However, the mechanism by which V. unguiculata exhibits anti-diabetic activity has not been explored. Based on the previously reported activities, the bioactive extract of the V. unguiculata has been evaluated against DM using in vitro studies, network pharmacology, molecular docking, MD simulation, and binding free ener- gies with the intention to explore the potential anti-diabetic mechanism. In this study, the key approach was adopted to explore the pathways, which plays crucial role in the patho- genesis of the DM, i.e., by inducing damage to the insulin producing cells and confer insulin resistance such as PTGS2, Glycogen Synthases Kinase 3 Beta (GSK3B) and Toll-like recep- tor-4 (TLR-4) signaling (13-15). The GSK3B has been focused recently on diabetes management due to its involvement in insulin resistance, insulin synthesis, and protection of the B-cells (Islet of Langerhans cells). Similarly, the role of TLR-4 has also been implicated in the pathogenesis of various dis- eases, including diabetes; enhanced TLR-4 activity paves the way for inflammation of various tissues, including the pan- creas, and cross-talk with multiple signaling pathways such as MAPKs, NF-kB, and JAK/STAT signaling (16,17). Primarily, in this study, V. unguiculata plant bioactive compounds were employed against the multiple signaling pathways involved in Diabetes pathogenesis. By employing comprehensive computational approaches, it was assumed that bioactive compounds from the V. unguiculata interact with the mul- tiple biological signaling pathways concerned with glucose metabolism, insulin resistance, and the inflammatory pro- cess, which will eventually improve diabetes symptoms by regulating the blood glucose level (13-15). Currently, several classes of anti-diabetic drugs are in clin- ical practice for the management of DM. However, the exist- ing drugs are associated with various unwanted side effects such as weight gain, lactic acidosis, urinary tract infections, and the cases of DM are increasing globally. Thus, there is a need for the exploration of new drugs, which are not only effective but also associated with fewer side effects. Natural products offer a cheap source for new drug development, and they can be used to discover drugs that fight various dis- eases, including DM (18,19). The use of V. unguiculata plant as an anti-diabetic has been reported traditionally; thus, based on the previous reported studies, it was anticipated that V. unguiculata would be effective against DM. Methodology Plant material collection, identification, and extraction In August 2022, a fresh whole plant (14kg) of V. unguicu- lata was gathered from Village Rustam, district Mardan, Khyber Pakhtunkhwa, Pakistan. Dr. Mohib Shah from the Department of Botany, AWKUM, authenticated and identi- fied the plant specimen by comparing it with existing speci- mens at the Department of Botany, AWKUM, and the flora of Pakistan. The Department of Botany, AWKUM, assigned Voucher No. AWKUM.Bot.100.31.3.9 to the plant specimen. After cleaning, fresh plant material was dried in the shade at room temperature. The entire dried plant was crushed, resulting in 838 gm of powdered plant. The powdered plant was macerated using a hydroalcoholic solvent (90 % methanol and 10% distilled water) following the proce- dure described by Mistriyani et al. (20). The procedure was repeated three times to achieve the highest extraction pos- sible. The solvent was evaporated using a rotary evaporator Sardar et al Drug Target Insights 2025; 19: 73 © 2025 The Authors. Published by AboutScience - www.aboutscience.eu to concentrate the filtrate. The concentrated extract was then dried in an oven at approximately 40 °C. Ultimately, 186g of oily, viscous extract was acquired and stored in a refrigerator at 2-8 °C. Fractionation Distilled water at a warm temperature was added to the hydro-methanolic extract. The mixture was then sepa- rated using n-hexane in a separating funnel. The remaining methanol extract was then fractionated using ethyl acetate to yield n-hexane, ethyl acetate, and water fractions. This process was repeated multiple times to obtain the maximum fractions. The fractions were further concentrated using a vacuum rotary evaporator and then dried in an oven at approximately 40 °C. In-vitro anti-diabetic activity The in vitro anti-diabetic activities of both the crude hydro-methanolic extract and the subsequent fractions were examined. α-amylase inhibitory activity The already established technique was used to con- duct the alpha amylase inhibitory assay (21). In a 96-well microplate, 5 μL of the enzyme solution and 15 μl of sam- ple, diluted in phosphate buffer at various concentrations, were added. The reaction was initiated with the addition of 20 μl starch solution and then incubated for 10 minutes at 37 °C, followed by further incubation at 37 °C for 30 minutes. HCl (10 μl of 1M) and iodine reagent (75 μL) were added to each well to stop the reaction. Acarbose at a concentration of 64 μg/mL served as the positive control, and a blank was created using phosphate buffer (pH 6.9). The absorbance at 580 nm was measured, and the percentage inhibitory activity was calculated using the provided equation: % Inhibition = Abs of Control – Abs of sample /Abs of control x 100 α-glucosidase inhibitory activity The already established method for measuring alpha- glucosidase inhibitory activity was used (22). Test samples at different concentrations (20 μl), alpha-glucosidase (1 U/ml) (10 μl), and phosphate buffer (100 mM, pH = 6.8) (50 μl) were combined and allowed to preincubate for 15 minutes at 37°C in a 96-well microplate. Following the addition of P-nitrophenyl glucopyranoside (5 mM) as the substrate (20 μl), the mixture was incubated for 20 minutes at 37°C. The reac- tion was stopped by adding 50 μl of 0.1 M sodium carbonate (Na2CO3). The absorbance of the liberated p-nitrophenol was determined at 405 nm using a microplate reader. Acarbose was utilized as a standard in different concentrations. Each experiment was conducted in triplicate, with a parallel setup that did not contain any test sample. The % inhibitory activity was calculated using the provided equation: % Inhibition = Absorbance of Control – Absorbance of sample /Absorbance of control x 100 Aldose reductase inhibitory assay The evaluation of aldose reductase inhibitory activity was conducted using a previously established method (23) with some modifications. Upon euthanasia, the eyes of normal Swiss albino mice were promptly extracted, and the lenses were subsequently cleansed with normal saline. The weights of the lenses were then re-determined. Once transparent lenses were obtained, a 10 % homogeneous solution was prepared in 0.1 M phosphate buffer saline (pH 7.4). This solu- tion was then subjected to centrifugation at 5000 × g for 10 minutes. The resulting supernatant was collected and stored on ice. The protein content of the lens homogenate was determined. A sample cuvette containing 0.7 mL of phos- phate buffer, 0.1 mL of NADPH (25 × 10−5 M), 0.1 mL of lens supernatant, and 0.1 mL of DL-glyceraldehyde (5 × 10−4 M) was utilized to assess the aldose reductase inhibitory activ- ity of the fractions, with a final volume of 1 mL. Absorbance measurements were taken in comparison to a reference cuvette devoid of DL-glyceraldehyde. The pH of the reaction mixture was adjusted to 6.2, and the enzymatic reaction was initiated upon mixing the substrate with the solution mixture. The absorbance was then monitored at 340 nm for three minutes at 30-second intervals. The extract was dissolved in phosphate buffer saline (PBS) to produce a stock solution containing all the fractions. Cuvettes treated with 0.1 mL of each fraction from various stock solutions, with final concen- trations of 10, 25, 50, 100, 200, and 300 µg/mL, were used as reference and standard cuvettes to evaluate aldose reduc- tase inhibitory activity. The process was initiated by adding 0.1 mL of DL-glyceraldehyde. The percentage inhibitions of the aldose reductase assay for the extract were calculated assuming that the lens of a normal mouse exhibited 100 % activity. IC50 values were calculated for each sample. Phytocompounds Screening Active compounds of V. unguiculata were identified from existing literature and several databases of biologically active phytochemicals, including Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) (24), KNApSAcK (25). The keyword’ Vigna unguiculata’ was used for database searches, while a comprehensive literature review was con- ducted using PubMed and Google Scholar. The bioavailability (F) and drug-likeness (DL) of all ingredients were assessed to determine their ADME (absorption, distribution, metabolism, and excretion) properties. Compounds were selected based on a DL value of at least 0.18 and an OB value of at least 30%. The OB and DL values for all active compounds were calculated using SwissADME and ADMETlab. Additionally, chemical information such as CID number, structure, and molecular weight was collected from PubChem and ChemSpider. Retrieval of potential targets of selected compounds Swiss Target Prediction (26) and the STITCH database (27) were used to predict the potential targets of selected com- pounds. These compounds were uploaded to the STITCH database with the options set to ‘Homo sapiens’ and using screening criteria as combined scores 0.7 or higher. Further, In silico Anti-diabetic Potential of Vigna unguiculata74 © 2025 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti the SMILES notation of each compound was inputted into the Swiss Target Prediction web application to predict tar- gets by employing the reverse pharmacophore mapping approach and predicted scores with a probability score of 0.7. In network pharmacology, this prediction is very com- monly employed for the generation of possible compound- target interactions using available data. The validation of the predicted targets and subsequently comparing them with the known DM target is crucial. Hence, the reliability of the pre- dicted targets was enhanced using publicly available curated databases, i.e., GeneCards and DisGeNET databases and the predicted targets of the STICTH database were compared. Microarray data analysis In order to validate the findings of the study, three microar- ray datasets, including GSE22435, GSE43950, and GSE92724, were obtained from the NCBI GEO database (Online) (28). GEO is a high-throughput gene expression data repository, includ- ing microarray and hybridization arrays. In this study, DEGs were analyzed using the Limma R package. Limma is widely known for its efficient tools for reading, normalizing, and ana- lyzing gene expression data for DEGs, and hence, it is the first preferred choice for high-throughput and microarray data. Screening criteria to identify candidate genes were adjusted p-value less than 0.05 and absolute value of log (Fold Change) greater than 1.0 but less than −1.0. In the R program, we used the ggplot2 package to create a volcano plot to differentiate between statistically significant and nonsignificant genes. Pathway and Functional Enrichment Analysis The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was employed to carry out functional annotation and enrichment analysis (29). Key targets were examined using DAVID to predict functions across three cat- egories: biological process (BP), molecular function (MF), and cellular component (CC). A threshold of an adjusted p-value ≤ 0.05 was used, and the top 10 Gene Ontology (GO) enrich- ments and the top 10 KEGG pathways with the highest counts were selected for further exploration. Network Construction Network analysis was conducted to elucidate the mech- anisms through which V. unguiculata affects T2DM. This analysis was facilitated by Cytoscape 3.8.0 (30), a software platform that provides tools for importing, visualizing, and analyzing biomolecular interaction networks. In the con- structed network, the nodes are the target genes, and the edges are the interactions between the active compounds and the target genes. To define the network topology, the degree centrality of each compound, target gene, or path- way was calculated using the Network Analyzer of Cytoscape. Targets that showed the maximum level of interconnected- ness were termed ‘key targets’. Protein–Protein Interaction Network Construction PPIs are essential because they are highly flexible, tunable, and selective processes. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (31) was then used to search for the functional relationship between the above identified key targets, which requires the combined score to be more than 0.9, while the high confidence score was applied to enhance the reliability of the data. A high threshold was selected to ensure that interactions include all the relevant potential interactions to enhance the reliability of the network interaction. These interactions involved the use of experimental data such as protein homology, or physical type of interaction, and co-expression data, with the intention to make a more biologically relevant network by employing these additional filters. The obtained PPI network was then subjected to the next step of filtration and analysis using the Cytohubba plu- gin in Cytoscape to identify essential regulatory genes and potential targets. Virtual screening The protein targets identified during the Protein-Protein Interaction Network were then further validated through molecular docking, which is a rapid computational technique. Information about the candidate target was obtained from the X-ray crystal structures offered under the RCSB Protein Data Bank (32). Structural optimization of the complex was done by UCSF Chimera (33), while protein targets and prob- able binding sites were predicted using the CASTp site map- ping tool (34). Virtual screening of the reported compounds was performed using the AutoDock Vina in Ubuntu (35,36) in order to assess the binding of the core targets with the active compounds. The favorable docked conformations were considered in terms of binding energies. Docking scores, which reflect the strength and stability of the compound- target interactions, were used as primary criteria for select- ing promising compounds and their targets. The results of the AutoDock vina were validated using the PyRx software to cross-check the predictive score of the ligand and protein interaction. This approach ensures that the docking results are robust using multiple docking methods. Visualization of these interactions was achieved using Discovery Studio (37), highlighting the dynamics of compound-protein interactions in a visual format. MD simulation After the virtual screening, the molecular dynamic simulation was performed to assess structural as well as dynamic stability of the ligand-protein complexes for 50 ns using GROMACS software 2024.1 (38). The TIP3P water box was used for solvation, and the CHARMM27, i.e., all-atom force field, was applied. The ligand topologies were gener- ated using the SwissParam software. The system was mini- mized for 50000 steps to remove the steric clashes using the steepest descent model. Following the energy minimization, the system was equilibrated in two steps, i.e., NVT and NPT ensembles by applying the periodic boundary condition in all directions (36). During the NVT and NPT ensembles, the tem- perature was maintained at 300 K for 100 ps, while the NPT ensemble was maintained at 100 ps by keeping the pressure at 1 bar. The final MD run was performed for 100 ns, and the https://www.ncbi.nlm.nih.gov/geo/ Sardar et al Drug Target Insights 2025; 19: 75 © 2025 The Authors. Published by AboutScience - www.aboutscience.eu results were analyzed using various dynamical and structural properties (39). Binding free energy calculations To assess the binding free energy for all the complexes, MM-PBSA and MM-GBSA were performed (40). The binding free energy was calculated over the 100 ns simulation using MD trajectories, and the per-residue decomposition analy- sis was performed for both approaches, i.e., MM-PBSA and MM-GBSA, to determine the individual residues’ contribution towards the binding energy of the complexes. The binding free energy calculation was performed for 1000 frames (3000- 4000), keeping the interval of 2. The various parameters that were assessed include EEL (Electrostatic energy), ESURF (Electrostatic surface potential energy), ENPOLAR (Polar sol- vation energy), GGAS, Van der Waals, and total energy (40). Statistical analysis The experiments were performed three times, and the results are presented as mean ± standard error of the mean. The IC50 values were determined using IBM SPSS Statistics 21 software. The data underwent analysis using one-way ANOVA, followed by post hoc Dunnett’s multiple comparison test using GraphPad Prism version 8.0.2. The criteria for the statistical significance were chosen as a p-value less than 0.05. Dunnett’s post hoc was chosen because it adjusts for multiple comparisons inherently and tries to ensure the family-wise error rate control during multiple group comparisons with a single control while requiring no additional corrections. The effect sizes were applied to all ANOVA calculations for the magnitude effects quantifications. The various tests applied include the Epsilon-squared, Eta-squared, Random-effect, and Omega-Squared calculations with confidence intervals of 95 % to give comprehensive insight into the magnitude of effect. Results α-amylase inhibitory assay The inhibitory efficacy of crude hydro-methanolic extract of V. unguiculata and its fractions against α-amylase enzyme was investigated using in vitro activity. Various concen- trations, including 8, 4, 2, 1, 0.5, 0.25, 0.125, 0.0625, and 0.03125 mg/mL, were used to test the samples and deter- mine their IC50 values. The crude extract exhibited a signifi- cantly lower IC50 value of 0.202 mg/mL compared to the aqueous (0.279 mg/mL), ethyl acetate (0.557 mg/mL), and n-hexane (1.294 mg/mL) fractions. In comparison, Acarbose, used as the standard, demonstrated the highest inhibition of the alpha amylase enzyme with an IC50 value of 0.043 mg/ml, as depicted in Figure 1. α-glucosidase inhibitory assay The inhibitory potential against α-glucosidase enzyme using in vitro activity was examined on the crude hydro- methanolic extract of V. unguiculata and its fractions. Various concentrations, including 8, 4, 2, 1, 0.5, 0.25, 0.125, and 0.0625 mg/mL, were used to test the samples, and their IC50 values were determined. The crude extract displayed a sig- nificantly lower IC50 value (0.111 mg/mL) compared to the ethyl acetate (0.135 mg/mL), aqueous (0.243 mg/mL), and n-hexane (0.629 mg/mL) fractions among all the tested sam- ples. Acarbose, used as the standard, exhibited the highest FIGURE 1 - Representing alpha amylase inhibitory activity of (a) Crude extract, (b) Aqueous fraction, (c) Ethyl acetate frac- tion, (d) n-Hexane fraction, and (e) standard drug, Acarbose. In silico Anti-diabetic Potential of Vigna unguiculata76 © 2025 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti inhibition of alpha amylase enzyme with an IC50 value of 0.034 mg/mL, as depicted in Figure 2. Aldose reductase inhibitory assay V. unguiculata crude hydro-methanolic extract and its fractions were examined for their ability to inhibit aldose reductase enzyme through an in vitro assay. Various FIGURE 2 - Representing α- glucosidase inhibitory activity of (a) Crude extract, (b) Ethyl acetate fraction, (c) Aqueous fraction, (d) n-Hexane frac- tion, and (e) standard drug, Acarbose. concentrations, including 300, 200, 100, 50, 25, and 10 µg/mL, were tested, and the IC50 values were determined. The crude extract displayed the lowest IC50 value (15.823 µg/mL) among all the samples tested, followed by ethyl acetate (25.806 µg /mL), aqueous (52.376 µg /mL), and n-hexane (106.857 µg /mL) fractions. Quercetin, used as the standard, exhibited the highest inhibition of alpha amylase enzyme with an IC50 value of 8.545 µg /ml, as indicated in Figure 3. FIGURE 3 - Representing al- dose reductase inhibitory activity of (a) Crude extract, (b) Ethyl acetate fraction, (c) Aqueous fraction, (d) n-hexa- ne fraction, and (e) standard drug, Quercetin. Sardar et al Drug Target Insights 2025; 19: 77 © 2025 The Authors. Published by AboutScience - www.aboutscience.eu Network Pharmacology Screening of potential compounds The screening of potential compounds from V. unguicu- lata identified several candidates with drug-like potential, as summarized in Table 1. The compounds belong to various classes, including flavonoids, phenolic acids, isoflavones, triterpenoids, phytosterols, and alkaloids, each exhibit- ing different molecular weights, drug likeness (DL), and oral bioavailability (OB). Among the flavonoids, Quercetin, Kaempferol, Catechin, Luteolin, and Apigenin were identi- fied, with molecular weights ranging from 270.24 to 302.23 g/mol. These compounds showed moderate drug likeness scores, with Catechin having the highest DL of 0.64, and all of them exhibited an oral bioavailability of 0.55, indicat- ing their potential for oral administration. Protocatechuic acid, a phenolic acid with a molecular weight of 154.12 g/ mol, displayed a lower drug likeness score of 0.23 but a slightly higher oral bioavailability of 0.56, suggesting that while its drug-like properties may be limited, it could still be bioavailable if administered orally. Isoflavones such as Genistein, Daidzein, and Glycitein were also included in the screening. These compounds have molecular weights between 254.24 and 284.26 g/mol and exhibit moderate drug likeness, with Genistein having a DL of 0.44. Like the flavonoids, they all have an oral bioavailability of 0.55. In the category of triterpenoids, Betulinic acid stands out with a significantly higher molecular weight of 456.7 g/mol, a drug likeness score of 0.25, and a notably higher oral bioavail- ability of 0.85, suggesting its strong potential as an orally administered drug candidate. The phytosterols identified include Beta-sitosterol, Campesterol, and Stigmasterol, with molecular weights around 400 to 414 g/mol. These compounds showed varying drug likeness scores, with Beta- sitosterol achieving the highest DL of 0.78, and all three exhibiting an oral bioavailability of 0.55. Finally, Vicine, an alkaloid with a molecular weight of 304.26 g/mol, demon- strated the highest drug likeness score of 0.84 among the compounds screened, coupled with an oral bioavailability of 0.55, highlighting it as a particularly promising candidate for further drug development. Overall, the screening results indicate that several compounds from V. unguiculata pos- sess favorable drug-like properties and oral bioavailability, making them viable candidates for further investigation in drug development efforts. Similarly, the comprehensive pharmacokinetic studies of the selected compounds, includ- ing Stigmasterol, Quercetin, Luteoline, and Acarbose, are shown in the Supplementary Table 1. TABLE 1 - Summarizes the list of compounds obtained from V. unguiculata with drug-like potential Compound Compound Class Molecular Weight (MW) Drug Likeness (DL) Oral Bioavailability (OB) Quercetin Flavonoid 302.23 0.52 0.55 Kaempferol Flavonoid 286.24 0.5 0.55 Catechin Flavonoid 290.27 0.64 0.55 Luteolin Flavonoid 286.24 0.38 0.55 Apigenin Flavonoid 270.24 0.39 0.55 Protocatechuic acid Phenolic Acid 154.12 0.23 0.56 Genistein Isoflavone 270.24 0.44 0.55 Daidzein Isoflavone 254.24 0.29 0.55 Glycitein Isoflavone 284.26 0.37 0.55 Betulinic acid Triterpenoid 456.7 0.25 0.85 Beta-sitosterol Phytosterol 414.7 0.78 0.55 Campesterol Phytosterol 400.7 0.59 0.55 Stigmasterol Phytosterol 412.7 0.62 0.55 Vicine Alkaloid 304.26 0.84 0.55 Acarbose Oligosaccharide 645.608 0.17 3.4.2. Microarray Data Analysis Microarray gene expression data were analyzed using datasets obtained from the GEO database. Additionally, the GSE22435 dataset contained data from 7 affected indi- viduals and 10 healthy controls (Fig. 4A and 4D). Differential expression analysis was conducted using the LIMMA pack- age, resulting in the identification of a significant number of differentially expressed genes (DEGs) across these datasets. Specifically, the analysis of GSE22435 revealed 671 upregu- lated and 198 downregulated genes, leading to a total of 870 DEGs. In the GSE43950 dataset, 2,133 DEGs were identified (Fig. 4B and 4E), and the GSE92724 dataset yielded 1,160 DEGs (Fig. 4C and 4F). All identified DEGs met the stringent criteria of an adjusted p-value less than 0.05 and an absolute log fold change (|log(FC)|) of greater than or equal to 1. These find- ings underscore the substantial alterations in gene expression In silico Anti-diabetic Potential of Vigna unguiculata78 © 2025 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti between affected individuals and controls, providing a robust basis for further biological interpretation and validation. Target Identification A comprehensive target identification process was carried out, resulting in the identification of 1,310 targets for 14 active compounds using the STITCH and SwissTarget Prediction databases. These targets were then cross-referenced with the DEGs identified through the microarray data analysis. This comparison revealed 88 common genes that are impli- cated in both the disease context and the plant-derived com- pounds (Fig. 5A). The compound-target network analysis demonstrated that these 88 genes were targeted by multiple compounds, indicating their potential as druggable targets. Network Analysis Following the identification of the 88 common genes, a protein-protein interaction (PPI) network was constructed (Fig. 5B). The network consisted of 88 nodes and 144 edges, illustrating the complex interactions among these genes. Further analysis identified the top 10 genes with the high- est degree of connectivity within the network (Fig. 5C), which included EGFR (degree score of 43), SRC (41), PTGS2 (39), MMP9 (33), BCL2 (33), TLR4 (29), GSK3B (26), PTPRC (24), KIT (22), and BCL2L1 (20). Due to their high connec- tivity, these genes may play central roles in the disease mechanism and serve as critical targets for therapeutic interventions. FIGURE 4 - Differential Gene Expression Analysis for GEO Datasets GSE22435, GSE43950, and GSE92724. (A), (B), and (C) show UMAP plots, boxplots of gene expression, and adjusted p-value distributions for each dataset. (D), (E), and (F) pre- sent volcano plots highlighting upregulated (red) and downre- gulated (blue) genes. Sardar et al Drug Target Insights 2025; 19: 79 © 2025 The Authors. Published by AboutScience - www.aboutscience.eu Functional Annotation Later, the functional annotation of these 88 common genes was performed using the DAVID software, which revealed their involvement in multiple diabetes-related pathways. The analysis identified significant clusters of genes associ- ated with key biological processes (Fig. 6), including meta- bolic pathways such as prostanoid and fatty acid biosynthesis. Specifically, genes were enriched in processes like long-chain fatty acid metabolic processes, organic acid biosynthetic pro- cesses, and eicosanoid metabolic processes. Additionally, another cluster highlighted the role of these genes in retinoid metabolic processes and response to retinoic acid, empha- sizing their involvement in important signaling pathways. Moreover, a smaller subset of genes was associated with oxi- doreductase activity, particularly acting on CH-OH groups of donors and the incorporation of two atoms of oxygen, indicat- ing their role in oxidation-reduction processes. In terms of molecular function, the common genes were linked to activities such as ligand-activated transcription fac- tor activity and nuclear receptor activity, which are crucial for regulating gene expression, particularly in response to retinoic acid. Other identified functions include protein auto- phosphorylation, essential for signal transduction, and pro- tein tyrosine kinase activity, which plays a vital role in various cellular signaling pathways. The GO analysis also revealed that these genes are integral components of critical cellular structures, such as the Schaffer collateral-CA1-CA1 synapse, highlighting their role in synaptic transmission and plasticity in the brain. Additionally, other genes were located in the tertiary granule lumen and the intrinsic component of the presynaptic membrane, emphasizing their involvement in neurotransmitter release and synaptic function. Further KEGG pathway analysis was also performed (Fig. 7). One of the most prominent pathways identified is the Arachidonic acid metabolism, which is crucial for the syn- thesis of bioactive lipid mediators that play significant roles in inflammation and cellular signaling. Another critical path- way is the HIF-1 signaling pathway, which is essential for cel- lular responses to hypoxia and is often implicated in cancer progression. The PPAR signaling pathway was also depicted, which plays an important role in regulating fatty acids and glucose homeostasis and is relevant to metabolic diseases. It also marks down pathways that relate to particular diseases, for example, non-small cell lung cancer or Bladder cancer; this means that these genes are involved directly in causing these types of cancer or in their advancement. Moreover, the information about the discovered VEGF signaling pathway, which plays an important role in the formation of new blood vessels and is widely used in cancer treatment. Other path- ways under study include NF-kappa B signaling, via which most immune responses are controlled, Th17 cell differentia- tion during autoimmune diseases, etc. The occurrence of the Adherens junction pathway was also observed for cell-cell adhesion, underlining the importance of these genes in sup- porting the cell’s structure. In general, the KEGG pathway analysis shows that the common genes are found in various signaling pathways and biological processes that are known to be important for maintaining cellular homeostasis and regulating meta- bolic and disease processes. This type of analysis is help- ful in understanding the possible functions of these genes from a human disease perspective and might inform future directions on treatments and cures. Apparently, the network representation strengthens the claims made with respect to FIGURE 5 - Network and Target Analysis of Common Genes. (A) Venn diagram showing the overlap between compound- related targets (3,186) and disease-related targets (327), identifying 88 common genes. (B) Protein-protein interaction (PPI) network of the 88 com- mon genes, illustrating the complex interactions among these targets. (C) Sub-network highlighting the top 10 genes based on degree of connecti- vity In silico Anti-diabetic Potential of Vigna unguiculata80 © 2025 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti FIGURE 6 - Gene Ontology Analysis of 88 common ge- nes representing their in- volvement in multiple bio- logical processes, cellular components, and molecular functions. the connectivity of these genes in the designated GO classes and proves the multifaceted nature of these genes in terms of their functions in various biological processes, molecular functions, and cellular components. Compound-target pathway network The compound-target pathway network analysis (Fig. 8) revealed that GSK3B, PTGS2, and TLR4 are the primary target proteins, displaying the highest connectivity within the network and interacting with the most bioactive com- pounds. The high degree of connectivity values means that these proteins are core to the network and highlight them as potential targets for treatment. Furthermore, the investiga- tion also pointed to the fact that many compounds, including those from V. unguiculata, interact with these proteins, mak- ing them valuable targets for therapeutic agents. Notably, GSK3B, PTGS2, and TLR4 are known to be associated with type 2 diabetes; GSK3B was reported to be a key protein regulating insulin signaling and glucose homeostasis, PTGS2 participates in inflammation aggravating insulin resistance, and TLR4 was reported to be involved in immune response and chronic inflammation in the development of T2D. These observations indicate that modulating the expression of GSK3B, PTGS2, and TLR4 using the active compounds isolated from V. unguiculata may be a potential therapeutic approach for type 2 diabetes. Molecular docking analysis The docking results provide insights into the binding affinities of various compounds with three target proteins: GSK3B, TLR4, and PTGS2, as shown in Table 2. These inter- actions are critical for evaluating the efficacy of these com- pounds as inhibitors or modulators of these proteins. The Virtual screening results showed Stigmasterol had the high- est binding affinity with the 8AV1 (GSK3B), Luteolin exhibited the highest binding affinity with the 4PH9 (PTGS2), while Quercetin showed the highest binding energy with the 3FXI (TLR4). The ligand-protein complexes were further validated using MD simulation and binding free energy calculations. During the virtual screening of the compounds with the 4PH9, Sardar et al Drug Target Insights 2025; 19: 81 © 2025 The Authors. Published by AboutScience - www.aboutscience.eu FIGURE 7 - KEGG Pathway analysis demonstrating the involvement of selected genes in diabetes relevant pathways. FIGURE 8 - Compound-Target- Pathway network. The green octagon represents the com- pound, the red circle repre- sents the hub genes, and the blue square represents the pathways targeted by hub ge- nes. In silico Anti-diabetic Potential of Vigna unguiculata82 © 2025 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti the center of the grid box includes x = 20.685, y = 31.547, and z = 27.584, while the size of the grid box includes x = 80, y = 102, and z = 72. Similarly, the virtual screening of the com- pounds with the 8AV1, the center of the grid box was kept as x = −3.818, y = 0.936, and z = −2.868, while the size of the grid box was kept as x = 60, y = 64, and z = 72. Furthermore, the virtual screening of the compounds with the 3FXI, the center of the grid box was kept as x = 4.536, y = −17.363, and z = 20.067, while the size of the grid box was kept as x = 60, y = 40, and z = 100. The molecular docking of Luteoline with the 4PH9 showed three hydrogen bonds (ASNA39, GLUA466, and GLYA136), two pi-alkyl bonds (LEUA153 and PROA154), two carbon-hydrogen bonds (CYSA47 and ARGA470), and one unfavorable acceptor-acceptor bond (TYRA131). The molecular docking of the Stigmasterol with the 8AV1 showed seven pi-alkyl bonds (ILEA37, TYRA109, LEUA163, ALAA58, VALA45, LYSA60, and LEUA153), and one carbon-hydrogen bonds (ASPA175). The molecular docking of Quercetin with 3FXI showed one hydrogen bond (LYSA204), two pi-anion bonds (ASPA155 and ASPA183), one carbon-hydrogen bond (HISA153), and one unfavorable acceptor-acceptor bond (SERA183). The molecular docking of Acarbose was con- ducted against the 8AV1, 3FXI, and 8AV1, and results showed that the binding energy of Acarbose with the 8AV1 is −6.9 kcal/mol, with 3FXI is −6.7 kcal/mol, and with the 4PH9 is −8.9 kcal/mol. The Acarbose showed the highest binding energy against the inflammatory target, i.e., PTGS2; however, against the GSK3B and TLR-4, the Acarbose showed lower binding energy as compared to the other compounds Table 3. The compounds and protein interactions are shown in three- dimensional and two-dimensional views using Discovery Studio visualizer_16 (Fig. 9). MD simulation analysis During the entire 100 ns, it was found that all ligand- protein complexes remained stable during the simulation, and no significant deviation in the structural conformation of the ligand-protein complex was noticed. The ligand-protein complex stability was measured using various key parameters such as RMSD, RMSF, RoG, etc., and it was noticed that within the first few ns, the system reached equilibrium and there- after remained stable. The RMSD values were calculated to assess the convergence of the system to its equilibrium con- formation. During the RMSD calculation, initially, the system showed deviation for a few ns due to thermodynamic shock, but thereafter, the RMSD showed no significant fluctuations for all the complexes, as shown in Figure 10. The RMSD anal- ysis showed that the average RMSD of the 3FXI_Quercetin was 0.25828, the average RMSD of the 4PH9_Luteoline was 0.45871, while the average RMSD of the 8AV1_Stigmasterol was 0.28030 nm. The RMSF analysis was conducted to study the behavior, i.e., flexibility of the individual amino acid of the protein following binding with the ligand. The RMSF analysis showed that complexes remain stable during the simulation, and no significant fluctuations were noticed, as shown in Figure 11. The RMSF analysis showed that the average RMSF of the 3FXI_Quercetin was 0.14277, the average RMSF of the 4PH9_Luteoline was 0.10841, while the average RMSF of the 8AV1_Stigmasterol was 0.11547 nm. The compactness of the protein structure following ligand binding was observed using the radius of gyration (RoG). The RoG analysis showed that the complexes remained stable during the entire simu- lation, which further validated the results of the RMSD and RMSF. Minor fluctuations have been observed in the com- plexes, which correlate with the small-scale motions and con- formational changes associated with the ligand binding with proteins Figure 12. The RoG analysis showed that the average of the 3FXI_Quercetin complex was 3.1447, the average RMSF of the 4PH9_Luteoline was 2.4579, while the average RoG of the 8AV1_Stigmasterol was 2.1969 nm. The changes in the ligand-protein complex surface following exposure to the sol- vent during the 100 ns simulation were studied using SASA. TABLE 2 - Docking results of various compounds with 8AV1 (GSK3B), 3FXI (TLR4), and 4PH9 (PTGS2) proteins. The table summarizes the binding affinities (in kcal/mol) Compound Name Binding Affinity (kcal/mol) Binding Affinity (kcal/mol) Binding Affinity (kcal/mol) Stigmasterol −8.854 −8.679 −7.244 Campesterol −8.833 −8.062 −7.084 Beta-sitosterol −8.739 −8.052 −7.149 Daidzein −8.198 −7.088 −6.667 Quercetin −8.146 −9.122 −7.237 Apigenin −8 −8.941 −7.066 Luteolin −7.982 −9.297 −6.885 Kaempferol −7.782 −8.619 −6.732 Genistein −7.685 −8.81 −6.865 Glycitein −7.645 −7.297 −6.864 Catechin −7.637 −8.098 −7.159 Vicine −6.17 −7.04 −6.479 Acarbose −6.9 −6.7 −8.9 Sardar et al Drug Target Insights 2025; 19: 83 © 2025 The Authors. Published by AboutScience - www.aboutscience.eu Initially, the SASA showed minor fluctuations, and thereafter the complexes achieved stability as shown in Figure 13. The hydrogen bonds analysis was conducted during the entire simulation to study the stability of ligand-protein complexes. The number of hydrogen bonds fluctuated for all complexes during the course of the simulation; however, the maximum number of hydrogen bonds was retained during the simula- tion, as shown in Figure 14. Binding Free Energy parameters The MM-PBSA and MM-GBSA analyses were performed for all the complexes to determine their stability in terms of binding energy. During the binding free energy calculations of the 8AV1_Stigmastrol using MM-PBSA revealed that the van der Waals energy, EEL, ENPOLAR, and GGAS remain negative and exhibited favorable binding energy. However, EPB energy and GSOLV energy of the system remain positive and indicate unfavorable binding free energy. However, the total energy of the system remains negative and indicates favorable binding energy, as shown in Table 3 and Supplementary Figure 1. Similarly, the MM-PBSA results were validated using the MM-GBSA analysis. During the binding free energy calculations of the 8AV1_ Stigmastrol using MM-GBSA revealed that the van der Waals energy, EEL, ESURF, and GGAS remain negative and exhib- ited favorable binding energy. However, the EGB energy and GSOLV energy of the system remain positive and indicate unfavorable binding free energy. However, the total energy of the system is negative and indicates favorable binding energy, as shown in Table 4 and Supplementary Figure 2. The MM-PBSA and MM-GBSA analyses were performed for all the complexes to determine their stability in terms of binding energy. During the binding free energy calculations of the 4PH9_Luteoline using MM-PBSA revealed that the van der Waals energy, EEL, ENPOLAR, and GGAS remain negative and exhibited favorable binding energy. However, EPB energy and GSOLV energy of the system remains positive and indicate unfavorable binding free energy. However, the total energy of the system remains negative and indicates favorable binding FIGURE 9 - Molecular docking analysis of key compounds with target proteins TLR4 (3FX1), PTGS2 (4PH9), and GSK3B (8AV1). Based on the virtual screening, stigmaste- rol showed binding affinity with the GSK3B, luteoline exhibited the highest binding energy with the PTGS2, and quercetin showed the highest binding energy with the TLR4. The interaction of the ligands with the target proteins was presented in two-dimensional and three-dimensional views using Discovery Studio Visua- lizer. In silico Anti-diabetic Potential of Vigna unguiculata84 © 2025 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti FIGURE 10 - The RMSD para- meter following the MD si- mulation using the compound with the highest binding affi- nity. The stigmasterol showed the highest binding affinity with the 8AV1, Luteolin with 4PH9, and Quercetin with the 3FXI. The RMSD analysis show- ed that all complexes showed an acceptable value of RMSD, and no significant changes were observed. FIGURE 11 - The RMSF analysis was performed for all the complexes following the simulation. The results show- ed no major fluctuation in the RMSF value, and the results indicate that all complexes re- main stable. energy, as shown in Table 5 and Supplementary Figure 3. Similarly, the MM-PBSA results were validated using the MM-GBSA analysis. During the binding free energy calculations of the 4PH9_ Luteoline using MM-GBSA revealed that the van der Waals energy, EEL, ESURF, and GGAS remain negative and exhib- ited favorable binding energy. However, the EGB energy and GSOLV energy of the system remains positive and indicate unfavorable binding free energy. However, the total energy of the system is negative and indicates favorable binding energy, as shown in Table 6 and Supplementary Figure 4. The MM-PBSA and MM-GBSA analyses were performed for all the complexes to determine their stability in terms of binding energy. During the binding free energy calculations of the 3FXI_Quercetin using MM-PBSA revealed that the van der Waals energy, EEL, ENPOLAR, and GGAS remain nega- tive and exhibited favorable binding energy. However, EPB energy and GSOLV energy of the system remains positive and indicate unfavorable binding free energy. However, the total energy of the system remains negative and indicates favor- able binding energy, as shown in Table 7 and Supplementary Figure 5. Similarly, the MM-PBSA results were validated using the MM-GBSA analysis. During the binding free energy calculations of the 3FXI_ Quercetin using MM-GBSA revealed that the van der Waals energy, EEL, ESURF, and GGAS remain negative and exhib- ited favorable binding energy. However, the EGB energy and GSOLV energy of the system remains positive and indicate unfavorable binding free energy. However, the total energy of the system is negative and indicates favorable binding energy, Sardar et al Drug Target Insights 2025; 19: 85 © 2025 The Authors. Published by AboutScience - www.aboutscience.eu FIGURE 12 - The RoG of all the complexes was determined following the simulation to assess the stability of the com- plexes. The RoG results sho- wed that 8AV1_Stigmasterol, and 4PH9_Luteolin complexes are lowered as compared to the 3FXI_Quercetin complex. FIGURE 14 - The 3FXI_Querce- tin showed the highest num- ber of hydrogen, i.e., five, as compared to the 8AV1_Stig- masterol and 4PH9_Luteolin complex. FIGURE 13 - The 8AV1_Sti- gamsterol result revealed the lowest SASA as compared to the 4PH9_Luteolin and 3FXI_ Quercetin complexes. In silico Anti-diabetic Potential of Vigna unguiculata86 © 2025 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti as shown in Table 8 and Supplementary Figure 6. The bind- ing free energy calculations revealed that 8AV1_Stigmaterol showed the highest total binding free energies (MM-PBSA = -23.51, MM-GBSA = −31.52), followed by the 3FXI_Quercetin (MM-PBSA = −22.27, MM-GBSA = −30.56), and the lowest binding free energies were shown by the 4PH9_Luteoline (MM-PBSA = −10.38, MM-GBSA = −19.91). The contribution of the individual residues was deter- mined using per-residue decomposition analysis for all the complexes. The per-residue decomposition analysis showed that VAL45 and ILE37 were the highest energy contribut- ing amino acids in the 8AV1_Stigmasterol complex using MM-PBSA and MM-GBSA, respectively, Supplementary Figure 7. Meanwhile, in 4PH9_Luteoline complex decompo- sition analysis, it was revealed that CYS36 and PRO154 were the highest contributing amino acids using MM-PBSA and MM-GBSA parameters, respectively, Supplementary Figure 8. The per-residue decomposition analysis of the 3FXI_ Quercetin showed that GLU128 was the highest contributing residue using MM-PBSA and MM-GBSA analysis, as shown in the Supplementary Figure 9. Discussion The use of natural remedies as additional treatments alongside conventional therapies for the treatment of TABLE 3 - The MM-PBSA result of the 8AV1_Stigamsterol complex Frames VDWAALS EEL EPB ENPOLAR GGAS GSOLV TOTAL Average −39.97 −8.1 29.08 −4.52 −48.07 24.56 −23.51 SD 3.34 5.49 4.09 0.21 6.2 4.03 4.8 SEM 0.15 0.25 0.18 0.01 0.28 0.18 0.21 TABLE 4 - The MM-GBSA result of the 8AV1_Stigamsterol complex Frames VDWAALS EEL EGB ESURF GGAS GSOLV TOTAL Average −39.97 −8.1 21.8 −5.26 −48.07 16.54 −31.52 SD 3.34 5.49 3.5 0.45 6.2 3.36 4.2 SEM 0.15 0.25 0.16 0.02 0.28 0.15 0.19 TABLE 5 - The MM-PBSA result of the 4PH9_Luteolin complex Frames VDWAALS EEL EPB ENPOLAR GGAS GSOLV TOTAL Average −30.05 −20.24 43.36 −3.45 −50.29 39.91 −10.38 SD 2.24 3.66 4.34 0.07 3.99 4.32 2.78 SEM 0.1 0.16 0.19 0 0.18 0.19 0.12 TABLE 6 - The MM-GBSA result of the 4PH9_Luteolin complex Frames VDWAALS EEL EGB ESURF GGAS GSOLV TOTAL Average −30.05 −20.24 35.12 −4.74 -50.29 30.38 −19.91 SD 2.24 3.66 3.43 0.17 3.99 3.34 2.03 SEM 0.1 0.16 0.15 0.01 0.18 0.15 0.09 TABLE 7 - The MM-PBSA result of the 3FXI_Quercetin complex Frames VDWAALS EEL EPB ENPOLAR GGAS GSOLV TOTAL Average −21.08 −46.49 48.11 −2.8 −67.58 45.31 −22.27 SD 2.85 5.84 4.45 0.06 4.95 4.45 2.8 SEM 0.13 0.26 0.2 0 0.22 0.2 0.13 Sardar et al Drug Target Insights 2025; 19: 87 © 2025 The Authors. Published by AboutScience - www.aboutscience.eu diabetes and its associated complications is increasing world- wide, with many plants in different countries known to have effects in lowering blood sugar (41). Indian literature from the past has documented the anti-diabetic properties of more than 800 plants, while ethnopharmacological research has shown that over 1200 plants can be used for their anti- diabetic effects (42). The primary reason for postprandial hyperglycemia is the action of α-amylase and α-glucosidase enzymes, which break down carbohydrates. The process of breaking down carbohydrates starts with α-amylase, which breaks the 1,4-glycosidic bonds in polysaccharides, convert- ing them into disaccharides. Subsequently, α-glucosidase transforms the disaccharides into monosaccharides, resulting in elevated blood glucose levels after meals (43). Delaying the digestion of carbohydrates and reducing postprandial glucose levels, inhibitors of alpha-amylase and alpha-glucosidase aid in controlling high plasma glucose levels. Investigators typi- cally seek out natural substances that can control elevated blood sugar levels by reducing the activity of alpha-glucosi- dase and alpha-amylase, leading to decreased adverse effects such as diarrhea, nausea, and abdominal pain (44). During an in vitro study, researchers analyzed cowpea protein hydrolysates (PH) and ultrafiltered peptide fractions (UFPF) for their ability to inhibit enzymes like α-amylase, α-glucosidase, and dipeptidyl peptidase IV. The findings indi- cated that both PH and UFPF demonstrated the most potent inhibitory activity against the enzymes under investigation (45). A different research study found that cowpea seed protein hydrolysates demonstrated greater effectiveness in inhibiting α-amylase (46). The efficacy of sorghum-cowpea composite biscuits in inhibiting starch hydrolysis enzymes like α-amylase and α-glucosidase was examined. This combi- nation could potentially contribute to the reduction of post- prandial hyperglycemia by targeting specific enzymes (47). Aldose reductase can be found in almost all mammalian cells, with significantly higher levels in organs such as the cornea, retina, lens, myelin sheath, kidney, and sciatic nerves. These organs are prone to diabetic complications (48). The accumu- lation of polyol in lens fibers, due to the heightened activity of the polyol pathway, results in the influx of water and the generation of osmotic stress. Consequently, this leads to tis- sue damage through osmotic swelling, changes in membrane permeability, and oxidative stress. The polyol pathway has garnered considerable focus in the clinical treatment of sec- ondary diabetes complications because of these outcomes (49). The breakdown of glucose through the polyol pathway by enzymes leads to the production of sorbitol, which is a harmful substance for tissues as it increases osmotic pres- sure and can cause tissue swelling. By inhibiting the activity of aldose reductase, which is mainly present in the lens’s epithelial cells, it is possible to prevent the development of cataracts (23). The literature does not contain any data on the in vitro anti-diabetic properties, such as the inhibition of α-amylase, α-glucosidase, and aldose reductase activities, of the entire V. unguiculata plant. Therefore, our current study aims to assess the ability of the whole V. unguicu- lata plant to inhibit alpha amylase, alpha glucosidase, and aldose reductase enzymes. Among all the samples tested, the crude extract exhibited the most significant inhibitory activity against alpha amylase, alpha glucosidase, and aldose reductase with IC50 values of 0.202 mg/mL, 0.111 mg/mL and 15.823 µg/mL, respectively. Moreover, the current study also used a network phar- macology approach to come up with a complex network con- necting the compounds of V. unguiculata and the potential protein targets. Unlike most previous studies that focus on single-target drugs, our approach considers multiple tar- gets and their interactions, including those with T2DM. For instance, previous works have defined involvement of pro- teins such as GSK3B in insulin signaling and glucose metab- olism, but this work affords elaborate information to how multiple compounds influence GSK3B, PTGS2, and TLR4 at the same time. This multiple-target approach is in harmony with modern practices in network pharmacology, where the impact of drug candidates on multiple interconnected biolog- ical targets is researched. The GO and KEGG study provides insight into important signaling, including PTGS2, GSK3B, and TLR4, which are influenced by the V. unguiculata constituents and evidently support the potential multi-target anti-diabetic activity. However, the multi-target analysis conducted in this study increases the likelihood of off-target effects and paves the way for undesired biological responses. Furthermore, the non-specific interactions of the studied protein with mul- tiple signaling protein may alter biological responses and produce adverse effects. This study focused on the computa- tional predictions and in vitro analysis; however, to validate the safety and specificity of the compounds, in vivo and in vitro studies will be required. GSK3B is implicated in insu- lin signaling and glucose homeostasis. Several studies have revealed that GSK3B knockdown promotes insulin signaling and corrects glucose intolerance in diabetic animal models. Inhibition of GSK3B is important for regulating hyperglyce- mia and insulin resistance since it is targeted by these inhibi- tors (50). Similarly, PTGS2, commonly known as COX-2, is an enzyme that plays a significant role in inflammation (51). Inflammatory processes play a crucial role in the evolution and progression of insulin resistance and consequent type 2 diabetes. Select members of the COX-2 enzyme have been TABLE 8 - The MM-GBSA result of the 3FXI_Quercetin complex Frames VDWAALS EEL EGB ESURF GGAS GSOLV TOTAL Average −21.08 −46.49 40.85 −3.84 −67.58 37.02 −30.56 SD 2.85 5.84 4.6 0.12 4.95 4.61 2.3 SEM 0.13 0.26 0.21 0.01 0.22 0.21 0.1 In silico Anti-diabetic Potential of Vigna unguiculata88 © 2025 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti studied as an anti-inflammatory, which may have beneficial effects on other metabolic complications related to diabetes. In contrast, TLR4 contributes to the immune system, and its overactivation is suggested to induce the inflammation that drives insulin resistance (52). It has been proposed that using specific inhibitors or antagonists of TLR4 may present a useful strategy for the modulation of chronic inflammation and the improvement of insulin resistance in type 2 diabetes. Multi-target therapy in this case entails the identification of a number of pathways or proteins that are related to the dis- ease and treating all of them at once. This is thought to be a better approach to treating various intricate conditions like type 2 diabetes by combating various causes. In the future, the findings of this study can provide a good foundation for subsequent research into the medi- cal value of compounds of extracts from V. unguiculata. In vivo studies can also be undertaken to validate the findings made in silico and further examine the PK/PD characteristics of these compounds. We also supposed that the other tar- get proteins may be required for further research, and they may discover the new multi-target drug for the better treat- ment of T2DM, especially GSK3B, PTGS2, and TLR4. Further insights on how these compounds collectively function might be helpful in designing improved combination thera- pies, which could be beneficial in patients. Hence, this work not only opens more evidence about network pharmacol- ogy as a strategy for searching the targets and new drugs’ designs but also shows the application of plant extracts as a treatment for chronic diseases. Conclusion In the present study, the V. unguiculata was evaluated for anti-diabetic potential using in vitro, network pharma- cology, and computational approaches. The results of the in vitro analysis using V. unguiculata phytochemicals showed significant activity against the diabetes targets such as α-amylase, α-glucosidase, and aldose reductase enzymes concerned with glucose metabolism. Furthermore, the net- work pharmacology studies showed 88 shared target genes associated with T2DM, and the possible drug targets, i.e., GSK3B, PTGS2, and TLR4 were deemed as hub genes in the PPI network. The in vitro and network pharmacology stud- ies were followed by the computational studies using virtual screening, MD simulation, and binding free energies calcu- lations. The virtual screening showed the phytochemicals, i.e., Quercetin, Luteolin, and Stigmasterol, with the highest binding energy against the diabetes targets. The MD simu- lation studies were conducted following the virtual screen- ing to assess the dynamic stability, and the results showed that the complexes remain stable. The binding free energy studies using MM-PBSA, and MM-GBSA exhibited negative and favorable binding energies, hence indicating the stabil- ity of the complexes. In conclusion, the study showed that V. unguiculata phytochemicals exhibited significant activity against the diabetes targets using in vitro, network pharma- cology, and comprehensive computational approaches; how- ever, to employ the V. unguiculata phytochemicals clinically additional studies such as comprehensive in-vivo approaches will be required. Limitations of the study The limitations of this study include the lack of in vivo validation despite the fact that the present study includes in vitro analysis, network pharmacology approaches, and com- prehensive computational approaches. In order to validate the results of the study, the in vivo studies, including phar- macokinetics, pharmacodynamics, and toxicities associated with the top hits, should be conducted. Furthermore, the study mainly focused on identifying the molecular targets and binding affinities; the stability of the compounds in the in vivo system, such as metabolism, protein binding, and solu- bility, was not studied. Similarly, the computational studies offer key insight into the ligand’s interaction with the target protein; however, the computational studies are influenced by various factors such as the type of force field used and the input of the structure provided. Furthermore, the study also lacks in vitro and in vivo studies to assess the off-target effect and to explore the safety profile of the compounds; thus, to establish the specificity, selectivity, and safety of the studied compounds, comprehensive in vitro and in vivo studies will be required. Authors contributors HS: Data curation, Writing – original draft; Fatima Noor: Data curation, Writing – original draft; SMS: Data analysis, Writing – original draft; JSAO: Data curation, Writing – origi- nal draft; AUK: Data curation, Writing – original draft, MD: Data curation, Writing – final draft; HK: Designing, supervi- sion, Writing – final draft Acknowledgment The authors are thankful to HEC Pakistan for financial support under project No: (Ref No. 20-16097/NRPU/R&D/ HEC/2021 2021). The authors extend their appreciation to the Princess Nourah Bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R13), Princess Nourah Bint Abdulrahman University, Saudi Arabia. Data availability statement The data that support the findings of the present study are available from the corresponding authors upon reason- able request. 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