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<p>G protein-coupled receptors (GPCRs) are the largest and most pharmacologically important family of membrane proteins, responsible for a wide range of physiological responses through the transmission of extracellular signals. Their vital role in human health makes them key targets, with many approved drugs acting by modulating GPCRs. Natural products, known for their complex structures and biological activity, remain a major source of GPCR-targeting agents. Recent advances in computer-aided drug design (CADD) have improved the discovery of GPCR ligands, including natural compounds, through both structure-based and ligand-based methods. These include molecular docking, de novo ligand design, pharmacophore modeling, 3D-QSAR, and AI-driven techniques such as machine learning and deep learning. This review explores how combining these approaches with post-docking techniques—like molecular dynamics simulations and binding energy calculations using MM/PB(GB)SA, thermodynamic integration (TI), and free energy perturbation (FEP)—can enhance lead optimization. It also highlights ongoing challenges in the field, such as receptor flexibility, ligand promiscuity, and the scarcity of high-resolution GPCR structures.</p>

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Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Abdullahi Ibrahim Uba

摘要

G protein-coupled receptors (GPCRs) are the largest and most pharmacologically important family of membrane proteins, responsible for a wide range of physiological responses through the transmission of extracellular signals. Their vital role in human health makes them key targets, with many approved drugs acting by modulating GPCRs. Natural products, known for their complex structures and biological activity, remain a major source of GPCR-targeting agents. Recent advances in computer-aided drug design (CADD) have improved the discovery of GPCR ligands, including natural compounds, through both structure-based and ligand-based methods. These include molecular docking, de novo ligand design, pharmacophore modeling, 3D-QSAR, and AI-driven techniques such as machine learning and deep learning. This review explores how combining these approaches with post-docking techniques—like molecular dynamics simulations and binding energy calculations using MM/PB(GB)SA, thermodynamic integration (TI), and free energy perturbation (FEP)—can enhance lead optimization. It also highlights ongoing challenges in the field, such as receptor flexibility, ligand promiscuity, and the scarcity of high-resolution GPCR structures.