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Structure-Activity Relationships and Drug Design01:28

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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G Protein-coupled Receptors01:15

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G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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G Protein–Coupled Receptors (GPCRs) are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to various stimuli. GPCRs regulate critical physiological pathways and are excellent drug targets for treating diseases such as diabetes, cancer, obesity, depression, or Alzheimer's. Nearly 35% of approved drugs implement their therapeutic effects by selectively interacting with specific GPCRs.
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Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
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AI meets physics in computational structure-based drug discovery for GPCRs.

Mayako Michino1,2, Jeremie Vendome3, Irina Kufareva4

  • 1Sanders Tri-Institutional Therapeutics Discovery Institute, New York, NY USA.

Npj Drug Discovery
|July 7, 2025
PubMed
Summary

Artificial intelligence is revolutionizing structure-based drug discovery for G protein-coupled receptors (GPCRs). AI models enhance hit discovery and lead optimization, offering new strategies for these challenging therapeutic targets.

Keywords:
Computational chemistryDrug discoveryLead optimizationReceptor pharmacologyStructure-based drug designVirtual screening

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Area of Science:

  • Pharmacology and Cheminformatics
  • Computational Biology and Drug Design

Background:

  • G protein-coupled receptors (GPCRs) represent a critical class of drug targets.
  • Structure-based drug discovery (SBDD) for GPCRs has historically faced significant challenges.
  • Traditional SBDD methods have limited success in identifying effective GPCR-targeting therapeutics.

Purpose of the Study:

  • To explore the transformative impact of artificial intelligence (AI) on GPCR drug discovery.
  • To detail the application of computational models in hit discovery and lead optimization for GPCRs.
  • To provide guidance on best practices for developing and validating predictive models in this field.

Main Methods:

  • Review of recent advancements in AI-driven computational modeling for GPCRs.
  • Analysis of AI's role in accelerating hit identification and lead optimization processes.
  • Discussion of methodologies for generating and validating predictive computational models.

Main Results:

  • AI-powered computational models are significantly improving the efficiency of GPCR drug discovery.
  • New avenues are opened for identifying and optimizing drug candidates targeting GPCRs.
  • Validated predictive models offer a promising approach for future therapeutic development.

Conclusions:

  • AI represents a paradigm shift in tackling the complexities of GPCR structure-based drug discovery.
  • Computational models are becoming indispensable tools for advancing GPCR-targeted therapeutics.
  • Adoption of best practices ensures the reliable application of AI in developing novel GPCR drugs.