ReviewCurrent opinion in structural biology2026
Computer-aided structural modeling and drug discovery for G-protein-coupled receptors in the age of artificial intelligence.
Review in Current opinion in structural biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
G-protein-coupled receptors (GPCRs) are a large family of membrane proteins that mediate cellular responses to diverse stimuli and serve as targets for ∼35 % of Food and Drug Administration-approved drugs. Their structural complexity, conformational heterogeneity, and membrane embedding have historically hindered experimental characterization, although advances in crystallization and cryogenic electron microscopy have expanded access to high-resolution receptor structures. In parallel, artificial intelligence (AI) has transformed protein modeling and drug discovery as recognized by the 2024 Nobel Prize in Chemistry. This minireview highlights recent applications of AI to GPCR research (2023-2025), including structure prediction, virtual screening, generative design of small molecules and protein binders, mechanistic studies using molecular dynamics, and systems-level analyses. Together, these approaches are reshaping GPCR biology and accelerating next-generation drug discovery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.