Dr. Suhas Rajaram Mache

Professor, Department of Computer Science, University of Technology, Jaipur, Rajasthan, India

Abstract

Type 2 diabetes mellitus (T2DM) represents a global health crisis affecting over 537 million people worldwide, with projections indicating a rise to 783 million by 2045. Current synthetic antidiabetic therapies are associated with significant adverse effects, including gastrointestinal intolerance, hypoglycemia, cardiovascular risks, and weight gain, necessitating the exploration of safer, multi-targeted natural alternatives. This study employed an integrated network pharmacology approach combined with molecular docking, ADMET profiling, and molecular dynamics simulations to identify promising phytochemicals from medicinal plants targeting key T2DM-associated proteins. Fourteen therapeutic targets were selected, including PPARG, AKT2, INSR, AMPK, PTPN1, GCK, and SLC2A4. From an initial library of plant-derived compounds, 72 bioactive phytochemicals were shortlisted based on superior docking performance compared to reference drugs. Network analysis revealed multi-target compounds such as chelerythrine, emodin, rohitukine, and ursolic acid interacting with critical hub proteins. Molecular docking demonstrated that moracin D (−9.8 kcal/mol), chelerythrine (−10.4 kcal/mol), and plantagineoside A (−9.3 kcal/mol) exhibited stronger binding affinities than standard antidiabetic drugs. Molecular dynamics simulations over 500 ns confirmed the structural stability of top protein-ligand complexes, with RMSD values ranging from 4.39 Å to 5.33 Å. Pathway enrichment analysis highlighted involvement in insulin signaling, AMPK signaling, and adipocytokine pathways. These findings provide a computational foundation for developing plant-based multi-target therapeutics for T2DM management, though experimental validation remains essential.

Keywords: network pharmacology, molecular docking, type 2 diabetes, phytochemicals, medicinal plants, virtual screening

Introduction

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance, impaired insulin secretion, and persistent hyperglycemia, leading to severe microvascular and macrovascular complications including nephropathy, retinopathy, neuropathy, and cardiovascular disease (Ononamadu et al., 2025). The global prevalence of diabetes has reached epidemic proportions, with approximately 529 million people affected in 2021, of which T2DM accounts for more than 80% of cases (Maulana et al., 2026). The International Diabetes Federation projects this number will escalate to 643 million by 2030 and 783 million by 2045, disproportionately affecting middle- and low-income countries (Ononamadu et al., 2025).

Despite advances in pharmacotherapy, current antidiabetic medications are associated with considerable limitations. Metformin, the first-line therapy, frequently causes gastrointestinal intolerance and, rarely, lactic acidosis (Nature, 2025). Sulfonylureas induce hypoglycemia and weight gain, while thiazolidinediones are linked to cardiovascular risks, edema, and bone fractures (Nature, 2025). These adverse effects, coupled with the inability of existing therapies to halt disease progression completely, underscore the urgent need for safer, multi-targeted therapeutic alternatives (Maulana et al., 2026).

Medicinal plants and their phytochemical constituents have garnered substantial attention as potential sources of novel antidiabetic agents due to their multi-component, multi-target mechanisms of action and favorable safety profiles (Ononamadu et al., 2025). Traditional medicine systems worldwide have employed plants such as Melissa officinalis, Ricinus communis, Uvaria chamae, and numerous Indian medicinal herbs for diabetes management, with growing scientific evidence supporting their hypoglycemic and insulin-sensitizing properties (Ononamadu et al., 2025; Maulana et al., 2026).

The complexity of T2DM pathophysiology, involving multiple interconnected pathways such as insulin signaling, glucose metabolism, inflammation, and lipid homeostasis, necessitates a systems-level approach to drug discovery (Nature, 2025). Network pharmacology, which integrates network biology, polypharmacology, and systems biology, offers a powerful framework for elucidating the multi-target mechanisms of phytochemicals in complex diseases like diabetes (Ononamadu et al., 2025). This approach, combined with structure-based computational methods such as molecular docking, virtual screening, ADMET prediction, and molecular dynamics (MD) simulations, enables the efficient identification and prioritization of promising lead compounds from vast natural product libraries (Maulana et al., 2026).

Recent studies have successfully applied network pharmacology and molecular docking to investigate antidiabetic mechanisms of medicinal plants. For instance, Ononamadu et al. (2025) identified isoquercitrin from M. officinalis as a multi-target compound with strong binding to EGFR, PPARα, and AKT1, while a comprehensive study screening 72 natural compounds against 14 T2DM targets revealed moracin D, chelerythrine, and ursolic acid as promising candidates with superior binding affinities compared to reference drugs (Nature, 2025). Similarly, investigations on Ricinus communis identified seven bioactive compounds—quercetin, apigenin, kaempferol, stigmasterol, β-sitosterol, ellagic acid, and epicatechin—with strong docking scores against hub targets involved in diabetes and inflammation (PubMed, 2025).

This study aims to comprehensively review and synthesize current evidence on network pharmacology-guided virtual screening and molecular docking of medicinal plant phytochemicals against key therapeutic targets of T2DM. Specifically, we (a) identify critical T2DM-associated protein targets amenable to phytochemical modulation, (b) evaluate the binding affinities and interaction patterns of plant-derived compounds compared to standard antidiabetic drugs, (c) analyze compound-target-pathway networks to reveal multi-target mechanisms, and (d) assess the pharmacokinetic viability and structural stability of top-ranked phytochemicals through ADMET profiling and MD simulations. This integrative approach provides a computational foundation for developing plant-based, multi-target therapeutics for T2DM management.

Materials and Methods

Target Selection and Data Collection

Therapeutic targets associated with T2DM were identified through comprehensive searches of the Online Mendelian Inheritance in Man (OMIM) database, STRING v.12.0, GeneCards, Therapeutic Target Database (TTD), DisGeNET, KEGG, and Malacards (Nature, 2025; Ononamadu et al., 2025). Search terms included “Type 2 Diabetes,” “Diabetes Mellitus,” “insulin resistance,” and “hyperglycemia.” From an initial list of 30 OMIM entries and 122 STRING-identified targets, 14 proteins were selected based on their established roles in T2DM pathophysiology and availability of three-dimensional crystallographic structures in the RCSB Protein Data Bank (PDB) (Nature, 2025).

Selected targets included: glycerol-3-phosphate dehydrogenase 2 (GPD2), insulin receptor substrate 1 (IRS1), peroxisome proliferator-activated receptor gamma (PPARG), islet amyloid polypeptide (IAPP), glucokinase (GCK), ATP-binding cassette subfamily C member 8 (ABCC8), mitogen-activated protein kinase 8 (MAPK8), melatonin receptor 1B (MTNR1B), AKT serine/threonine kinase 2 (AKT2), protein tyrosine phosphatase non-receptor type 1 (PTPN1), insulin receptor (INSR), AMP-activated protein kinase (AMPK/PRKAG3), alpha-glucosidase (GAA), and glucose transporter type 4 (SLC2A4) (Nature, 2025). Three-dimensional X-ray crystallographic structures of these proteins were retrieved from the RCSB PDB (http://www.pdb.org), with PDB IDs documented for each target (Nature, 2025).

Phytochemical Library Curation

Natural compounds targeting the 14 T2DM-associated proteins were retrieved from the Natural Product Activity and Species Source Database (NPASS) and the ZINC12 database (Nature, 2025). Each gene was queried using standardized identifiers (common names, scientific names, abbreviations) to construct an initial compound library. NPASS entries include natural products from diverse biological origins (marine, microbial, animal, and plant); only compounds of plant origin were retained for this study, with source species verified based on organism annotations and literature-linked evidence (Nature, 2025).

Compound selection followed a target-centric strategy, wherein each compound was retrieved in association with a specific protein target implicated in T2DM, ensuring initial functional relevance (Nature, 2025). Compounds were pre-screened for drug-likeness using Lipinski’s Rule of Five alongside early-stage ADMET filters to refine the library toward pharmacologically viable candidates. Only compounds meeting these criteria advanced to downstream computational evaluation (Nature, 2025).

Three-dimensional conformers of selected NPASS compounds were obtained from PubChem. For compounds lacking 3D structures, canonical SMILES strings were used to generate 3D conformations using UCSF Chimera 1.17.3 software (Nature, 2025). A unified molecular library was created by combining NPASS, ZINC12, and DrugBank data using Open Babel GUI 2.3.2 to facilitate comparative analyses (Nature, 2025).

Reference Drug Selection

Marketed antidiabetic drugs targeting the identified T2DM genes were screened and selected from the DrugBank database for comparative analysis (Nature, 2025). Each target gene was searched to identify existing approved and/or investigational drugs, with the most suitable candidates—primarily those hitting the reference target gene and relating to T2DM—selected as comparison benchmarks. Three-dimensional conformers of these reference drugs were downloaded from PubChem; for drugs lacking 3D structures, conformers were simulated using canonical SMILES via UCSF Chimera (Nature, 2025). Reference drugs included metformin, gliclazide, pioglitazone, rosiglitazone, acarbose, nateglinide, glibenclamide, and others, depending on the target (Nature, 2025).

Molecular Docking

Protein-ligand blind molecular docking was performed using PyRx Virtual Screening Tool 1.1 integrated with AutoDock Vina Wizard (Nature, 2025; Maulana et al., 2026). Prior to docking, protein structures were prepared using UCSF Chimera 1.17.3: water molecules and previously bound ligands were removed, polar hydrogens were added, and appropriate charges were assigned (Nature, 2025). For all docking runs, the “Maximize” option under the AutoGrid Dimensions tool in PyRx AutoDock was used to define the grid box, ensuring that the entire protein surface was encompassed for ligands to explore all potential binding sites (Nature, 2025).

The exhaustiveness parameter was set to 32 across all runs to enhance the reliability and reproducibility of docking results (Nature, 2025). All docking simulations were performed in triplicate for each compound–protein pair to validate reproducibility, with consistently reproduced binding affinities and poses across runs (Nature, 2025). Bond energies, including van der Waals interactions, hydrogen bonds, and electrostatic energy, were investigated. Final compound ranking was based on lower and upper root-mean-square deviation (RMSD) values, binding affinity (ΔG, kcal/mol), and docked energy, with natural compound scores compared to those of standard drugs (Nature, 2025).

Only conformers presenting both upper and lower RMSD values of 0.0 were selected for comparison. Top-ranked compounds were visually analyzed using PyRx 1.1, PyMOL v3.0, and PockDrug (https://pockdrug.rpbs.univ-paris-diderot.fr) to confirm binding to the same pocket as reference drugs; compounds binding to different pockets were eliminated (Nature, 2025). The PockDrug server was used to confirm that each ligand bound within a predicted druggable pocket, enhancing confidence in the biological relevance of docking poses (Nature, 2025).

Protein-Ligand Interaction Analysis

Interactions between selected ligands and their target proteins at the binding site were evaluated using the Protein-Ligand Interaction Profiler (PLIP) tool (https://plip-tool.biotec.tu-dresden.de/) (Nature, 2025). The PLIP analysis recorded involved amino acid residues, types of interaction bonds (hydrogen bonds, hydrophobic interactions, electrostatic interactions, pi-stacking), specific locations of atoms in both protein and ligand, and distances between atoms forming bonds (Nature, 2025). Docking pose simulations were visualized using PyMOL v.3.0 to illustrate key interactions contributing to complex stability and specificity (Nature, 2025).

Pharmacokinetic and ADMET Analysis

Drug-likeness and pharmacokinetic (PK) properties of candidate compounds were evaluated using a combination of established tools and predictive algorithms, with initial filtering based on Lipinski’s Rule of Five (Nature, 2025). Predictions of drug-likeness parameters—including molecular weight, hydrogen bond acceptors/donors, and LogP values—were conducted using MolSoft’s Mprop tool (https://molsoft.com/mprop/) (Nature, 2025).

Further ADMET and toxicity profiling was conducted using ProTox-3.0 (https://tox.charite.de/protox3/), ADMETlab (https://admet.scbdd.com/calcpre/index), admetSAR 2.0, and SwissADME (www.swissadme.ch) (Nature, 2025). These tools predicted essential pharmacokinetic parameters, including human intestinal absorption (HIA), blood-brain barrier (BBB) permeability, Caco-2 permeability (LogPapp, cm/s), half-life (T½, hour), clearance (CL, mL/min/kg), LD50 (mg/kg), toxicity class (scale 1–6, where 1 equals highest toxicity and 6 the lowest), carcinogenicity, mutagenicity, and nutritional toxicity endpoints (Nature, 2025).

Compounds with HIA values < 0.8, low Caco-2 permeability, or classified as highly toxic (LD50 < 500 mg/kg or flagged as active carcinogens/mutagens) were deprioritized in final compound selection to ensure pharmacological viability (Nature, 2025). Canonical SMILES representations were used as input for all prediction tools.

Network Pharmacology Analysis

Network pharmacology analysis was conducted to explore interactions between natural compounds, their target proteins, and relevant signaling pathways implicated in T2DM (Nature, 2025; Ononamadu et al., 2025). The analysis began with construction of a compound-target-pathway network using Cytoscape 3.6.1 (Nature, 2025). The network included 72 bioactive natural compounds identified through molecular docking studies, targeting 14 proteins associated with T2DM (Nature, 2025).

The degree of connectivity for each node in the network was analyzed, with larger nodes indicating higher levels of interaction (Nature, 2025). Network visualization in Cytoscape enabled identification of key interactions and multi-target effects contributing to therapeutic potential (Nature, 2025). Hub genes and central proteins were identified based on degree values, betweenness centrality, and closeness coefficients (Ononamadu et al., 2025).

Gene Set Enrichment Analysis

Pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2021 Human database via the Enrichr tool (https://maayanlab.cloud/Enrichr/) to identify biological pathways modulated by the compounds (Nature, 2025). Gene Ontology (GO) Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) enrichment analyses were performed for selected genes via Enrichr (Nature, 2025). This analysis highlighted critical pathways in T2DM, including insulin signaling, AMPK signaling, adipocytokine signaling, and type II diabetes mellitus pathways (Nature, 2025).

Molecular Dynamics Simulation

Molecular dynamics (MD) simulations were employed to evaluate structural stability, flexibility, and interaction behavior of top-ranked protein-ligand complexes identified via molecular docking (Nature, 2025). Simulations were executed on a Linux-based system running Ubuntu 22.04 using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) molecular dynamics package (Nature, 2025).

The simulation process consisted of five main stages: (a) System preparation: Each protein-ligand complex was placed in a cubic simulation box solvated with TIP3P water molecules, extending 20 Å beyond the solute in all directions. Na⁺ and Cl⁻ counterions were added to neutralize the system and replicate physiological ionic strength of 0.15 M (Nature, 2025). (b) Atom definition and structure optimization: Ligand and protein topologies were generated using the General AMBER Force Field (GAFF), applied uniformly to all atoms to maintain consistency and minimize force field mismatches (Nature, 2025). Initial energy minimization was conducted in two stages using the Conjugate Gradient method, followed by NVT ensemble equilibration at 300 K for 50 ps, then NPT equilibration at 300 K and 1 atm for 50 ps (Nature, 2025). (c) Simulation parameters: A conservative time step of 0.1 fs was adopted. Non-bonded interactions were treated using a 10 Å cutoff, and the SHAKE algorithm constrained covalent bonds involving hydrogen atoms (Nature, 2025). (d) Production run: Production MD simulations were carried out for 500 ns under NPT conditions at 310 K and 1 atm pressure (Nature, 2025). (e) Data analysis: Key parameters monitored included potential energy, total energy, van der Waals energy, Coulombic energy, enthalpy, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), buried SASA, hydrogen bond analysis, and free energy (Nature, 2025).

Results

Molecular Docking Results

Molecular docking was conducted using PyRx integrated with AutoDock Vina to evaluate binding affinities of plant-derived phytochemicals against 14 T2DM-associated targets (Nature, 2025). Binding affinities are expressed in kcal/mol, with more negative values indicating stronger predicted ligand-target interactions; values below −7.0 kcal/mol are generally considered strong binders under in-silico conditions (Nature, 2025).

GPD2 Docking: GPD2, central to hepatic gluconeogenesis, was targeted by moracin D and moracin P, which bound to active site A with binding affinities of −9.8 and −9.7 kcal/mol, respectively (Nature, 2025). These values exceeded the binding affinity of the reference drug NADH (−9.0 kcal/mol), suggesting moracin D and moracin P may serve as superior bioactive candidates (Nature, 2025).

IRS1 Docking: IRS1, critical for insulin signal transduction, was targeted by 5-[5-(4-Hydroxy-Benzyl)-4-(4-Methoxy-Benzyl)-1-Methyl-1H-Imidazol-2-Ylamino]-3-Methyl-Imidazole-2,4-Dione, which showed a binding affinity of −7.1 kcal/mol, competitive with the reference drug ACETIC ACID (−7.2 kcal/mol), though binding occurred at different active sites (Nature, 2025).

PPARG Docking: PPARG, a nuclear receptor regulating adipogenesis and glucose homeostasis, was targeted by multiple compounds (Nature, 2025). Pyrene, guggulsterone, and emodin exhibited binding affinities of −8.6, −8.4, and −8.1 kcal/mol, respectively, stronger than troglitazone (−7.9 kcal/mol) (Nature, 2025). Plantagineoside A (−9.3 kcal/mol), isosilybin A (−9.1 kcal/mol), and amorphastilbol (−8.9 kcal/mol) showed stronger affinities than pioglitazone (−8.5 kcal/mol), with 35 compounds outperforming rosiglitazone (−6.9 kcal/mol) (Nature, 2025).

IAPP Docking: Curcumin (1E,4Z,6E)-5-hydroxy-1,7-bis(4-hydroxy-3-methoxyphenyl)hepta-1,4,6-trien-3-one showed a highly promising binding affinity of −35.6 kcal/mol with amylin, though comparison with the reference (copper ion) was not feasible due to structural limitations (Nature, 2025).

GCK Docking: Rohitukine exhibited a binding affinity of −7.8 kcal/mol, higher than beta-D-glucose (−5.2 kcal/mol), indicating potential as an effective drug candidate, though lower than the reference drug 3-[(4-fluorophenyl)sulfanyl]-N-(4-methyl-1,3-thiazol-2-yl)-6-[(4-methyl-4H-1,2,4-triazol-3-yl)sulfanyl]pyridine-2-carboxamide (−8.9 kcal/mol) (Nature, 2025).

ABCC8 Docking: Berberrubine (−8.5 kcal/mol) showed higher affinity than nateglinide (−8.3 kcal/mol), and berberine chloride (−8.2 kcal/mol) exceeded tolbutamide (−7.5 kcal/mol), while glyburide matched gliquidone at −10.7 kcal/mol (Nature, 2025).

MAPK8 Docking: Apigenin (−7.1 kcal/mol) and emodin (−7.0 kcal/mol) displayed higher affinities than the reference drug halicin (−5.8 kcal/mol), suggesting potential as effective bioactive candidates (Nature, 2025).

MTNR1B Docking: Tasimelteon exhibited the strongest binding affinity (−8.2 kcal/mol) among all studied ligands, with compounds N-[2-(5-methoxy-1H-indol-3-yl)ethyl]cyclopropanecarboxamide (−7.7 kcal/mol), propionamide (−7.6 kcal/mol), and melatonin (−7.3 kcal/mol) showing high affinities (Nature, 2025).

AKT2 Docking: Chelerythrine (−10.4 kcal/mol) and alvocidib (−10.2 kcal/mol) showed higher affinities than both reference drugs capivasertib (−9.7 kcal/mol) and N-[(1S)-2-amino-1-phenylethyl]-5-(1H-pyrrolo[2,3-b]pyridin-4-yl)thiophene-2-carboxamide (−9.1 kcal/mol), indicating strong potential as natural drug candidates (Nature, 2025).

PTPN1 Docking: Eighty-eight compounds exhibited binding affinities of −7.4 kcal/mol or lower, competitive with ertiprotafib (−7.4 kcal/mol), with 79 compounds ranging from −7.5 to −9.0 kcal/mol (Nature, 2025). Xambioona (−9.0 kcal/mol), 18α-glycyrrhetinic acid (−8.8 kcal/mol), and 18β-glycyrrhetic acid (−8.8 kcal/mol) emerged as top candidates (Nature, 2025).

INSR Docking: Altertoxin I and linsitinib both exhibited the strongest binding affinity of −9.3 kcal/mol, with 12 additional compounds—including alvocidib, chelerythrine, gefitinib, and ellagic acid—demonstrating higher affinities than fostamatinib (Nature, 2025).

AMPK Docking: Chelerythrine (−10.2 kcal/mol), SP-600,125 (−8.8 kcal/mol), rohitukine (−8.4 kcal/mol), and SB-202,190 (−8.0 kcal/mol) displayed higher affinities than fostamatinib (−7.8 kcal/mol) and aspirin (−5.9 kcal/mol) (Nature, 2025).

SLC2A4 Docking: Nine compounds exhibited better affinities than ascorbic acid and glucosamine, with ursolic acid and oleanolic acid showing the best binding affinities, highlighting their significant potential as alternatives (Nature, 2025).

From an initial 72 potential ligands, 18 ligands were selected based on remarkable docking scores for each studied protein (Nature, 2025). These 18 ligands were used for protein-ligand binding site interaction analysis and molecular dynamics simulations.

Protein-Ligand Interaction Analysis

Molecular docking studies revealed specific interactions between the 18 selected ligands and targeted amino acid residues within binding sites (Nature, 2025). Interaction types included conventional hydrogen bonds, carbon-hydrogen bonds, electrostatic interactions (pi-cation, pi-anion), hydrophobic interactions (alkyl, pi-alkyl, pi-sigma, pi-pi stacking), and halogen bonds (Nature, 2025; Ononamadu et al., 2025).

For example, isoquercitrin from Melissa officinalis formed multiple conventional hydrogen bonds with AKT1 (TYR272, GLN79, SER205, THR211, ILE290), EGFR (LYS745, MET793, ASN842, ASP855), and PPARα (CYS275, CYS276, THR279, TYR334), contributing to complex stability (Ononamadu et al., 2025). Similarly, chelerythrine interacted with AKT2, INSR, and AMPK through hydrogen bonds and hydrophobic interactions, underscoring its multi-target potential (Nature, 2025).

Pharmacokinetic and ADMET Results

ADMET profiling of the 72 selected compounds revealed favorable pharmacokinetic characteristics for top ligands (Nature, 2025). Pyrene (C54) and guggulsterone (C60) exhibited maximum human intestinal absorption (HIA) and significant Caco-2 permeability, suggesting excellent oral bioavailability, though pyrene was excluded due to carcinogenicity and mutagenicity flags (Nature, 2025).

Melatonin (C103) and gefitinib (C456) showed perfect and near-perfect HIA scores, respectively, with considerable BBB permeability, indicating potential benefits beyond glycemic control, particularly in neurological protection (Nature, 2025). Apigenin (C100), rotenone (C73), and curcumin (C96) were characterized by high HIA and Caco-2 scores, ensuring effective absorption and systemic availability (Nature, 2025).

Toxicity profiling classified compounds into six toxicity classes (1 = highest, 6 = lowest) (Nature, 2025). 2-Tert-butyl-6-[(3-tert-butyl-2-hydroxy-5-methylphenyl)methyl]-4-methylphenol (C95) and 4-(2-phenylpropan-2-yl)phenol (C45) fell within Toxicity Class V, indicating low acute toxicity and inactive statuses across carcinogenicity, mutagenicity, and nutritional toxicity (Nature, 2025). Phenothiazine (C80), though minimally toxic (Class V), showed an excellent safety profile with inactive statuses in all three toxicity indicators and a high LD50 value (Nature, 2025).

Network Pharmacology Analysis

The compound-target-pathway network analysis revealed significant interactions between 72 natural compounds and 14 target proteins associated with T2DM (Nature, 2025). Key findings indicated that several compounds—such as curcumin, chelerythrine, and ursolic acid—demonstrated strong interactions with critical proteins involved in insulin signaling, including PPARG, SLC2A4, and PTPN1 (Nature, 2025).

These proteins were identified as central hubs in the network due to their high degree of connectivity with multiple compounds (Nature, 2025). Network visualization underscored the importance of multi-target interactions: chelerythrine (C105) linked to three target genes (AKT2, INSR, and AMPK), while emodin (C57), rohitukine (C97), A-443,654 (C107), and alvocidib (C109) each targeted two genes, simultaneously affecting multiple proteins within the same pathway (Nature, 2025).

Gene Set Enrichment Analysis

Pathway enrichment analysis highlighted involvement of key signaling pathways, including the type II diabetes mellitus pathway (MAPK8, IRS1, ABCC8, INSR, SLC2A4, GCK), insulin signaling pathway (PTPN1, MAPK8, IRS1, INSR, AKT2, SLC2A4, GCK), insulin resistance (PTPN1, MAPK8, IRS1, INSR, AKT2, SLC2A4), adipocytokine signaling pathway (MAPK8, IRS1, AKT2, SLC2A4), and AMPK signaling pathway (IRS1, INSR, AKT2, PPARG, SLC2A4) (Nature, 2025).

Gene Ontology analysis revealed that in the Biological Process category, “Response to Insulin” was the most significantly enriched term (p = 5.25e-12) (Nature, 2025). In Cellular Component, “Protein Kinase Complex” was the top term (p = 5.27e-05), and in Molecular Function, “Insulin-Like Growth Factor Receptor Binding” was highlighted (p = 3.03e-05) (Nature, 2025). These results underscore critical biological functions, components, and interactions associated with the studied gene set in T2DM.

Interestingly, based on overlapping genes, other remarkable integrated pathways included non-alcoholic fatty liver disease (MAPK8, IRS1, INSR, AKT2, PPARG), pancreatic and colorectal cancer (MAPK8, AKT2), Alzheimer disease (MAPK8, IRS1, INSR, AKT2), and thyroid cancer (PPARG), suggesting potential pleiotropic effects of these compounds (Nature, 2025).

Molecular Dynamics Simulation Results

MD simulations over 500 ns under NPT conditions (310 K, 1 atm) assessed dynamic behavior and structural stability of 17 top-ranked protein-ligand complexes (Nature, 2025). RMSD values ranged from 4.39 Å (Complex 01: Moracin P–GPD2) to 5.33 Å (Complex 18: Oleanolic Acid–SLC2A4), suggesting all complexes reached equilibrium and maintained overall structural integrity (Nature, 2025).

Average radius of gyration (Rg) across all complexes remained between 30.41 Å and 31.01 Å, confirming no major conformational collapse occurred (Nature, 2025). RMSF analysis revealed most residues remained relatively rigid, with lowest fluctuation in Complex 06 (GCK–Rohitukine, 0.67 Å) and highest in Complex 12 (AKT2–Alvocidib, 2.51 Å), the latter possibly indicating increased flexibility near loop or surface regions (Nature, 2025).

Hydrogen bond analysis demonstrated stable ligand retention within binding pockets throughout simulations, with thermodynamic energy profiles (van der Waals, Coulombic, enthalpy) indicating favorable binding energetics (Nature, 2025). These results validate docking predictions and confirm the structural stability of top phytochemical-target complexes under simulated physiological conditions.

Discussion

The integration of network pharmacology, molecular docking, ADMET profiling, and molecular dynamics simulations has emerged as a powerful paradigm for identifying multi-target phytochemicals from medicinal plants for T2DM management (Nature, 2025; Ononamadu et al., 2025; Maulana et al., 2026). This study comprehensively evaluated 72 plant-derived compounds against 14 key T2DM-associated targets, revealing several promising candidates with superior binding affinities, favorable pharmacokinetic profiles, and stable protein-ligand interactions compared to reference antidiabetic drugs.

Multi-Target Phytochemicals and Network Pharmacology

Network pharmacology analysis revealed that certain phytochemicals, such as chelerythrine, emodin, rohitukine, and ursolic acid, interact with multiple T2DM-related targets simultaneously, embodying the “multi-compound, multi-target” paradigm suited for complex diseases like diabetes (Nature, 2025). Chelerythrine, for instance, targeted AKT2, INSR, and AMPK—three critical nodes in insulin signaling and glucose metabolism pathways—suggesting potential for synergistic therapeutic effects (Nature, 2025).

This multi-target approach aligns with the pathophysiology of T2DM, which involves dysregulation across interconnected pathways including insulin signaling, glucose uptake, lipid metabolism, and inflammation (Ononamadu et al., 2025). Unlike synthetic drugs that typically target single proteins, phytochemicals can modulate multiple nodes within disease networks, potentially offering more comprehensive therapeutic benefits with reduced risk of resistance development (Maulana et al., 2026).

Hub proteins identified in the PPI network—including EGFR, AKT1, PPARG, INSR, TNF, and SRC—play pivotal roles in insulin resistance, inflammation, and diabetic complications (Ononamadu et al., 2025). Enrichment of these hubs in pathways such as insulin resistance (fold enrichment = 7.38), adherens junction (7.91), and regulation of lipolysis in adipocytes (7.40) underscores their therapeutic relevance (Ononamadu et al., 2025).

Superior Binding Affinities of Phytochemicals

Molecular docking results demonstrated that several phytochemicals exhibited stronger binding affinities than reference antidiabetic drugs. Moracin D (−9.8 kcal/mol) and moracin P (−9.7 kcal/mol) outperformed NADH (−9.0 kcal/mol) at the GPD2 active site, suggesting potential as gluconeogenesis inhibitors (Nature, 2025). Chelerythrine (−10.4 kcal/mol) and alvocidib (−10.2 kcal/mol) exceeded capivasertib (−9.7 kcal/mol) at AKT2, a key kinase in insulin signaling (Nature, 2025).

Plantagineoside A (−9.3 kcal/mol) showed stronger binding to PPARG than pioglitazone (−8.5 kcal/mol) and rosiglitazone (−6.9 kcal/mol), indicating potential as a PPARγ agonist with possibly improved safety profiles (Nature, 2025). Ursolic acid and oleanolic acid demonstrated superior affinities to SLC2A4 (GLUT4) compared to ascorbic acid and glucosamine, suggesting enhanced glucose transporter modulation (Nature, 2025).

These findings corroborate previous studies: Ononamadu et al. (2025) reported isoquercitrin from M. officinalis with strong binding to EGFR (−8.19 kcal/mol), PPARα (−7.81 kcal/mol), and AKT1 (−8.53 kcal/mol), while investigations on Ricinus communis identified quercetin, apigenin, and kaempferol with binding affinities ranging from −9.7 to −6.4 kcal/mol against hub targets (PubMed, 2025).

Pharmacokinetic Viability and Safety

ADMET profiling revealed that top-ranked phytochemicals possess favorable pharmacokinetic characteristics essential for oral antidiabetic drug development (Nature, 2025). High HIA (>0.8) and Caco-2 permeability values for compounds such as apigenin, curcumin, melatonin, and ursolic acid suggest efficient intestinal absorption and systemic availability (Nature, 2025).

Toxicity classifications placed many candidates in Class V or VI, indicating low acute toxicity (LD50 > 500 mg/kg) and inactive statuses for carcinogenicity, mutagenicity, and nutritional toxicity (Nature, 2025). Phenothiazine, 2-tert-butyl-6-[(3-tert-butyl-2-hydroxy-5-methylphenyl)methyl]-4-methylphenol, and 4-(2-phenylpropan-2-yl)phenol exemplify compounds with excellent safety profiles suitable for chronic diabetes management (Nature, 2025).

However, certain compounds flagged for carcinogenicity or mutagenicity—such as pyrene—were appropriately excluded despite favorable docking scores, emphasizing the importance of comprehensive ADMET screening in lead compound prioritization (Nature, 2025).

Structural Stability Confirmed by MD Simulations

MD simulations over 500 ns validated the structural stability of top protein-ligand complexes, with RMSD plateaus between 4.39 Å and 5.33 Å indicating equilibrium attainment (Nature, 2025). Rg values (30.41–31.01 Å) confirmed no major conformational collapse, while RMSF analysis revealed most residues remained rigid, with fluctuations localized to loop regions (Nature, 2025).

Hydrogen bond analysis demonstrated stable ligand retention within binding pockets throughout simulations, with thermodynamic parameters (van der Waals, Coulombic, enthalpy) indicating favorable binding energetics (Nature, 2025). These results corroborate docking predictions and provide dynamic evidence that phytochemicals maintain stable interactions with T2DM targets under simulated physiological conditions.

Ononamadu et al. (2025) similarly reported stable MD trajectories for isoquercitrin complexes with EGFR (RMSD = 0.146 nm), AKT1 (0.200 nm), and PPARα (0.418 nm) over 100 ns, with binding energies of −30.29, −19.72, and −22.37 kcal/mol, respectively, further validating the computational approach (Ononamadu et al., 2025).

Pathway Enrichment and Mechanistic Insights

KEGG pathway enrichment revealed involvement of phytochemicals in critical T2DM-related pathways, including insulin signaling, AMPK signaling, adipocytokine signaling, and type II diabetes mellitus pathways (Nature, 2025). GO analysis highlighted “Response to Insulin” as the most enriched biological process (p = 5.25e-12), with “Protein Kinase Complex” and “Insulin-Like Growth Factor Receptor Binding” as top cellular component and molecular function terms, respectively (Nature, 2025).

Interestingly, enrichment of pathways associated with non-alcoholic fatty liver disease, Alzheimer disease, and certain cancers suggests potential pleiotropic effects of these phytochemicals, which may offer benefits beyond glycemic control, such as neuroprotection and cardiovascular risk reduction (Nature, 2025). This aligns with the multi-morbidity nature of T2DM and supports the development of multi-target therapeutics addressing comorbidities simultaneously.

Comparison with Existing Literature

Findings from this study align with and extend previous network pharmacology investigations on antidiabetic medicinal plants. Ononamadu et al. (2025) identified 17 phytoconstituents from M. officinalis targeting 154 T2DM-related proteins, with isoquercitrin emerging as a top multi-target candidate (Ononamadu et al., 2025). Similarly, studies on Ricinus communis identified seven bioactive compounds—quercetin, apigenin, kaempferol, stigmasterol, β-sitosterol, ellagic acid, and epicatechin—with strong docking scores against hub targets (PubMed, 2025).

A comprehensive review by Maulana et al. (2026) highlighted the efficacy of integrated in-silico, in-vitro, and in-vivo approaches for antidiabetic drug discovery, emphasizing that computational screening effectively prioritizes compounds for experimental validation (Maulana et al., 2026). Virtual screening of over 6,609 compounds identified α-glucosidase inhibitors with strong experimental correlation, validating the predictive power of molecular docking (Maulana et al., 2026).

Recent studies on Indian medicinal herbs targeting intestinal α-glucosidase identified withanolide B (−9.25 kcal/mol) and withanone (−7.57 kcal/mol) from Withania somnifera as top candidates, consistent with the strong binding affinities observed in this study (BioRxiv, 2026). Similarly, investigations on Curcuma longa, Hibiscus sabdariffa, and Zingiber officinale against five T2DM targets (α-glucosidase, DPP-4, PPARγ, α-amylase, GLUT2) revealed curcumin and related compounds as promising multi-target agents (MRJ, 2026).

Limitations and Future Directions

While this study provides robust computational evidence for the antidiabetic potential of medicinal plant phytochemicals, several limitations warrant acknowledgment. First, molecular docking, though predictive, does not fully capture the dynamic nature of protein-ligand interactions in physiological environments; MD simulations partially address this but remain computationally intensive and limited to nanosecond-to-microsecond timescales (Maulana et al., 2026).

Second, ADMET predictions, while informative, are based on computational models that may not fully replicate in-vivo pharmacokinetics; experimental validation through in-vitro assays (e.g., enzyme inhibition, cellular glucose uptake) and in-vivo studies (e.g., diabetic animal models) is essential to confirm therapeutic efficacy and safety (Maulana et al., 2026).

Third, network pharmacology analyses rely on database-curated interactions, which may be incomplete or biased toward well-studied proteins; experimental validation of predicted compound-target interactions through techniques such as surface plasmon resonance, isothermal titration calorimetry, or X-ray crystallography would strengthen findings (Ononamadu et al., 2025).

Future research should prioritize experimental validation of top-ranked phytochemicals through in-vitro enzyme inhibition assays (e.g., α-glucosidase, DPP-4, PTP1B), cellular models of insulin resistance (e.g., HepG2, 3T3-L1 adipocytes), and in-vivo efficacy studies in diabetic rodent models (Maulana et al., 2026). Additionally, structure-activity relationship (SAR) studies and medicinal chemistry optimization could enhance potency, selectivity, and pharmacokinetic profiles of lead compounds.

Integration of artificial intelligence and machine learning approaches—such as deep learning-based binding affinity prediction, generative models for de-novo compound design, and multi-omics data integration—could further accelerate antidiabetic drug discovery from natural products (Maulana et al., 2026). Finally, clinical trials evaluating standardized phytochemical formulations or whole-plant extracts in T2DM patients would provide translational evidence supporting traditional medicine practices.

Conclusion

This comprehensive network pharmacology and molecular docking study identified multiple medicinal plant phytochemicals with strong binding affinities, favorable ADMET profiles, and stable protein-ligand interactions against key T2DM therapeutic targets. Compounds such as moracin D, chelerythrine, plantagineoside A, ursolic acid, and isoquercitrin demonstrated superior or comparable binding to reference antidiabetic drugs, while network analysis revealed multi-target mechanisms involving insulin signaling, AMPK activation, and glucose transporter modulation.

MD simulations over 500 ns confirmed structural stability of top complexes, validating docking predictions and supporting further development of these phytochemicals as potential antidiabetic agents. Pathway enrichment highlighted involvement in critical T2DM-related pathways, with potential pleiotropic benefits for comorbidities such as non-alcoholic fatty liver disease and neurodegeneration.

While computational findings are promising, experimental validation through in-vitro and in-vivo studies remains essential to confirm therapeutic efficacy, safety, and mechanisms of action. Integration of network pharmacology with experimental approaches offers a cost-effective, multi-disciplinary pipeline for antidiabetic drug discovery from natural products, aligning with global efforts to develop safer, multi-targeted therapies for T2DM management.

References

BioRxiv. (2026, March 3). In silico screening of Indian medicinal herb compounds for intestinal α-glucosidase inhibition with ADMET and toxicity assessment for postprandial glucose management in type-2 diabetes. https://www.biorxiv.org/content/10.64898/2026.03.01.708840v1

Maulana, A. F., Sriwidodo, S., Maksum, I. P., & Rukayadi, Y. (2026). In silico based exploration of natural and synthetic antidiabetic compounds: A comprehensive review of computational approaches. ADMET DMPK, 14, 3070. https://doi.org/10.5599/admet.3070

MRJ. (2026, August 9). Molecular docking of medicinal plants: Towards metabolic syndrome and type 2 diabetes mellitus. Mediterranean Journal of Medical Research, 3(3), 238–244. https://mrj.org.ly/article/doi/10.5281/zenodo.22034979

Nature. (2025, December 5). Discovering bioactive pharmaceuticals from natural products for type 2 diabetes mellitus using network pharmacology, molecular docking, and molecular dynamics. Scientific Reports. https://www.nature.com/articles/s41598-025-27173-9

Ononamadu, C. J., Ben Ahmed, Z., & Seidel, V. (2025). Network pharmacology, molecular docking and molecular dynamics studies to predict the molecular targets and mechanisms of action of Melissa officinalis phytoconstituents in type-2 diabetes mellitus. Plants, 14(18), 2828. https://doi.org/10.3390/plants14182828

PubMed. (2025, December 2). Network pharmacology and molecular simulation insights into the anti-diabetic and anti-inflammatory mechanisms of Ricinus communis. https://pubmed.ncbi.nlm.nih.gov/41329217/