ReviewTrends in pharmacological sciences2023
Targeting in silico GPCR conformations with ultra-large library screening for hit discovery.
Review in Trends in pharmacological sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Ultra-large library screening with an evolutionary algorithm in Rosetta (REvoLd).Communications chemistry · 2025Article
- Functional consequences of spatial, temporal and ligand bias of G protein-coupled receptors.Nature reviews. Nephrology · 2024Review
- Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease.Cell reports · 2024Article
- Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.Briefings in bioinformatics · 2024Article
- Regression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking.Journal of chemical information and modeling · 2024Article
- Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures.Molecules (Basel, Switzerland) · 2024Review
- Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties.Frontiers in molecular biosciences · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
The use of deep machine learning (ML) in protein structure prediction has made it possible to easily access a large number of annotated conformations that can potentially compensate for missing experimental structures in structure-based drug discovery (SBDD). However, it is still unclear whether the accuracy of these predicted conformations is sufficient for screening chemical compounds that will effectively interact with a protein target for pharmacological purposes. In this opinion article, we examine the potential benefits and limitations of using state-annotated conformations for ultra-large library screening (ULLS) in light of the growing size of ultra-large libraries (ULLs). We believe that targeting different conformational states of common drug targets like G-protein-coupled receptors (GPCRs), which can regulate human physiology by switching between different conformations, can offer multiple advantages.
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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.