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The global prevalence of diabetes, particularly Type 2 diabetes mellitus (T2DM), continues to rise, posing significant public health and economic burdens. Traditional treatments focus on established targets like PPAR, DPP-4, and SGLT2; however, limitations such as side effects and incomplete therapeutic efficacy have led to the investigation of emerging targets, including the MAPK and PI3K/Akt pathways and alpha-glucosidase inhibitors. In-silico drug discovery tools like AutoDock, MOE-Dock, and Biovia Discovery Studio have enabled precise molecular docking studies, while GROMACS facilitates molecular dynamics simulations to predict drug-receptor interactions. SwissADME, ProTox-II, and ADMETlab 3.0 ensure comprehensive ADMET profiling, while AI platforms such as NVIDIA’s BioNeMo and DeepChem offer advanced capabilities in virtual screening and multi-target drug design. This chapter explores these advancements, providing an overview of traditional and novel targets, the role of natural products, and the application of computational tools in designing innovative antidiabetic agents. It aims to bridge the gap between conventional pharmacology and modern in silico methodologies, helping to understand the way for personalized and effective diabetes therapies.

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Advances in the Design of Novel Antidiabetic Agents Using In-silico Approaches

  • 章节

  • 发表日期：

    2025年

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  • Sanket Jadhav,
  • Pranali Yeram,
  • Rajasekhar Reddy Alavala,
  • Amisha Vora

摘要

The global prevalence of diabetes, particularly Type 2 diabetes mellitus (T2DM), continues to rise, posing significant public health and economic burdens. Traditional treatments focus on established targets like PPAR, DPP-4, and SGLT2; however, limitations such as side effects and incomplete therapeutic efficacy have led to the investigation of emerging targets, including the MAPK and PI3K/Akt pathways and alpha-glucosidase inhibitors. In-silico drug discovery tools like AutoDock, MOE-Dock, and Biovia Discovery Studio have enabled precise molecular docking studies, while GROMACS facilitates molecular dynamics simulations to predict drug-receptor interactions. SwissADME, ProTox-II, and ADMETlab 3.0 ensure comprehensive ADMET profiling, while AI platforms such as NVIDIA’s BioNeMo and DeepChem offer advanced capabilities in virtual screening and multi-target drug design. This chapter explores these advancements, providing an overview of traditional and novel targets, the role of natural products, and the application of computational tools in designing innovative antidiabetic agents. It aims to bridge the gap between conventional pharmacology and modern in silico methodologies, helping to understand the way for personalized and effective diabetes therapies.