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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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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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Updated: Jun 12, 2025

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GPCRSPACE: A New GPCR Real Expanded Library Based on Large Language Models Architecture and Positive Sample Machine

Shiming Chen1, Feisheng Zhong1

  • 1Fujian Key Laboratory of Drug Target Discovery and Structural and Functional Research, School of Pharmacy, Fujian Medical University, Fuzhou 350122, China.

Journal of Medicinal Chemistry
|September 17, 2024
PubMed
Summary
This summary is machine-generated.

A new AI model, GPCR LLM, accelerates drug discovery by learning the chemical space of G protein-coupled receptor (GPCR) modulators. The resulting GPCRSPACE library offers improved synthesizability and diversity for developing novel GPCR therapeutics.

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Area of Science:

  • Medicinal Chemistry
  • Computational Drug Discovery
  • Pharmacology

Background:

  • G protein-coupled receptors (GPCRs) are critical drug targets, yet many lack selective modulators.
  • Developing novel GPCR therapeutics is essential due to their role in numerous physiological processes.

Purpose of the Study:

  • Introduce GPCRSPACE, a novel chemical library for GPCR drug discovery.
  • Leverage a new G protein-coupled receptors large language model (GPCR LLM) architecture for enhanced compound screening.

Main Methods:

  • Developed GPCR LLM using a positive sample machine learning strategy, avoiding negative sample construction.
  • Built the GPCRSPACE chemical library based on the GPCR LLM.
  • Evaluated GPCRSPACE for synthesizability, structural diversity, and GPCR-likeness.

Main Results:

  • GPCR LLM accelerates the identification and screening of potential GPCR-interactive compounds.
  • GPCRSPACE demonstrates superior synthesizability, structural diversity, and GPCR-likeness compared to existing datasets.
  • The positive-sample-only approach reduces false negatives and labeling time.

Conclusions:

  • GPCRSPACE represents a valuable resource for advancing GPCR drug discovery.
  • The GPCR LLM architecture offers an efficient approach to exploring GPCR chemical space.
  • This work addresses the need for selective modulators for under-targeted GPCRs.