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<p>The rising incidence of type 2 diabetes mellitus (T2DM) and the declining approval rate of new antidiabetic drugs highlight an urgent need for safer and more effective therapeutics. Partial PPARγ agonists have emerged as promising alternatives, offering efficacy with reduced side effects than full agonists. This study employed a comprehensive in silico strategy, combining ligand-based pharmacophore modeling, 3D-QSAR, ADME pre-filtering, virtual screening, molecular docking, MM-GBSA binding energy calculations, MD simulations, DFT analysis and toxicity predictions. A validated six-feature pharmacophore and robust 3D-QSAR model were developed from 71 known PPARγ agonists with reported antidiabetic activity. Virtual screening of ~ 600,000 ZINC compounds identified four promising hits, <b>CHEMBL1825121, CHEMBL4642973, CHEMBL4569907</b>, and <b>CHEMBL294165</b>. These compounds showed superior docking scores (− 10.919 to − 10.386&#xa0;kcal/mol) and MM-GBSA energies (− 85.9 to − 63.96&#xa0;kcal/mol) compared to the internal ligand SR145 (− 10.351&#xa0;kcal/mol, − 85.63&#xa0;kcal/mol) and standard drugs; rosiglitazone (− 7.272&#xa0;kcal/mol, − 48.14&#xa0;kcal/mol) and pioglitazone (− 7.033&#xa0;kcal/mol, − 47.21&#xa0;kcal/mol). Detailed docking analysis revealed key interactions with Arg288, Ser342, and Glu343, consistent with partial agonism, while avoiding strong AF-2 helix stabilization associated with full activation. MD simulations confirmed the stability of the ligand–PPARγ complexes over <b>500&#xa0;ns</b>, while DFT analysis revealed favorable electronic and chemical reactivity profiles. Among the four identified hits, <b>CHEMBL1825121</b> and <b>CHEMBL4569907</b> were identified as the top candidates, displaying strong binding affinity, high structural stability and favorable pharmacokinetic properties. While experimental validation remains essential, these findings provide a rational strategy for the development of next-generation PPARγ modulators for T2DM.</p>

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Rational in silico design of PPARγ agonists for type 2 diabetes: an integrated study using pharmacophore modeling, 3D-QSAR, molecular docking, MD simulations, DFT, and toxicity prediction

  • 期刊论文

  • 发表日期：

    2025年11月18日

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  • Tathagata Pradhan,
  • Ojasvi Gupta,
  • Gita Chawla

摘要

The rising incidence of type 2 diabetes mellitus (T2DM) and the declining approval rate of new antidiabetic drugs highlight an urgent need for safer and more effective therapeutics. Partial PPARγ agonists have emerged as promising alternatives, offering efficacy with reduced side effects than full agonists. This study employed a comprehensive in silico strategy, combining ligand-based pharmacophore modeling, 3D-QSAR, ADME pre-filtering, virtual screening, molecular docking, MM-GBSA binding energy calculations, MD simulations, DFT analysis and toxicity predictions. A validated six-feature pharmacophore and robust 3D-QSAR model were developed from 71 known PPARγ agonists with reported antidiabetic activity. Virtual screening of ~ 600,000 ZINC compounds identified four promising hits, CHEMBL1825121, CHEMBL4642973, CHEMBL4569907, and CHEMBL294165. These compounds showed superior docking scores (− 10.919 to − 10.386 kcal/mol) and MM-GBSA energies (− 85.9 to − 63.96 kcal/mol) compared to the internal ligand SR145 (− 10.351 kcal/mol, − 85.63 kcal/mol) and standard drugs; rosiglitazone (− 7.272 kcal/mol, − 48.14 kcal/mol) and pioglitazone (− 7.033 kcal/mol, − 47.21 kcal/mol). Detailed docking analysis revealed key interactions with Arg288, Ser342, and Glu343, consistent with partial agonism, while avoiding strong AF-2 helix stabilization associated with full activation. MD simulations confirmed the stability of the ligand–PPARγ complexes over 500 ns, while DFT analysis revealed favorable electronic and chemical reactivity profiles. Among the four identified hits, CHEMBL1825121 and CHEMBL4569907 were identified as the top candidates, displaying strong binding affinity, high structural stability and favorable pharmacokinetic properties. While experimental validation remains essential, these findings provide a rational strategy for the development of next-generation PPARγ modulators for T2DM.