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Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery
by
Hahn, Hyunggu
, Chung, Jason
, Flores-Espinoza, Emmanuel
, Thomsen, Alex R. B.
in
Algorithms
/ AlphaFold
/ Amino acids
/ Artificial Intelligence
/ Binding sites
/ computational docking
/ Computer applications
/ Crystallography
/ Drug development
/ Drug discovery
/ Drug Discovery - methods
/ Drugs
/ Electron microscopy
/ Experimental methods
/ G protein-coupled receptors
/ G proteins
/ GPCRs
/ Health aspects
/ Humans
/ Hypothesis testing
/ Innovations
/ Kinases
/ Ligands
/ Magnetic resonance spectroscopy
/ Membrane proteins
/ Molecular Docking Simulation
/ Nuclear magnetic resonance spectroscopy
/ Physiology
/ Protein Conformation
/ Protein structure
/ Protein structure prediction
/ Proteins
/ Receptors, G-Protein-Coupled - chemistry
/ Receptors, G-Protein-Coupled - metabolism
/ Research methodology
/ Review
/ RoseTTAFold
/ structure-based drug discovery
/ virtual screening
/ X-ray crystallography
2025
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Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery
by
Hahn, Hyunggu
, Chung, Jason
, Flores-Espinoza, Emmanuel
, Thomsen, Alex R. B.
in
Algorithms
/ AlphaFold
/ Amino acids
/ Artificial Intelligence
/ Binding sites
/ computational docking
/ Computer applications
/ Crystallography
/ Drug development
/ Drug discovery
/ Drug Discovery - methods
/ Drugs
/ Electron microscopy
/ Experimental methods
/ G protein-coupled receptors
/ G proteins
/ GPCRs
/ Health aspects
/ Humans
/ Hypothesis testing
/ Innovations
/ Kinases
/ Ligands
/ Magnetic resonance spectroscopy
/ Membrane proteins
/ Molecular Docking Simulation
/ Nuclear magnetic resonance spectroscopy
/ Physiology
/ Protein Conformation
/ Protein structure
/ Protein structure prediction
/ Proteins
/ Receptors, G-Protein-Coupled - chemistry
/ Receptors, G-Protein-Coupled - metabolism
/ Research methodology
/ Review
/ RoseTTAFold
/ structure-based drug discovery
/ virtual screening
/ X-ray crystallography
2025
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Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery
by
Hahn, Hyunggu
, Chung, Jason
, Flores-Espinoza, Emmanuel
, Thomsen, Alex R. B.
in
Algorithms
/ AlphaFold
/ Amino acids
/ Artificial Intelligence
/ Binding sites
/ computational docking
/ Computer applications
/ Crystallography
/ Drug development
/ Drug discovery
/ Drug Discovery - methods
/ Drugs
/ Electron microscopy
/ Experimental methods
/ G protein-coupled receptors
/ G proteins
/ GPCRs
/ Health aspects
/ Humans
/ Hypothesis testing
/ Innovations
/ Kinases
/ Ligands
/ Magnetic resonance spectroscopy
/ Membrane proteins
/ Molecular Docking Simulation
/ Nuclear magnetic resonance spectroscopy
/ Physiology
/ Protein Conformation
/ Protein structure
/ Protein structure prediction
/ Proteins
/ Receptors, G-Protein-Coupled - chemistry
/ Receptors, G-Protein-Coupled - metabolism
/ Research methodology
/ Review
/ RoseTTAFold
/ structure-based drug discovery
/ virtual screening
/ X-ray crystallography
2025
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Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery
Journal Article
Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery
2025
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Overview
Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, protein structures have been determined through experimental approaches such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy. While these methods are effective and are considered the gold standard, they are very resource-intensive and time-consuming, ultimately limiting their scalability. However, with recent developments in computational biology and artificial intelligence (AI), the field of protein prediction has been revolutionized. Innovations like AlphaFold and RoseTTAFold enable protein structure predictions to be made directly from amino acid sequences with remarkable speed and accuracy. Despite the enormous enthusiasm associated with these newly developed AI-approaches, their true potential in structure-based drug discovery remains uncertain. In fact, although these algorithms generally predict overall protein structures well, essential details for computational ligand docking, such as the exact location of amino acid side chains within the binding pocket, are not predicted with the necessary accuracy. Additionally, docking methodologies are considered more as a hypothesis generator rather than a precise predictor of ligand–target interactions, and thus, usually identify many false-positive hits among only a few correctly predicted interactions. In this paper, we are reviewing the latest development in this cutting-edge field with emphasis on the GPCR target class to assess the potential role of AI approaches in structure-based drug discovery.
Publisher
MDPI AG,MDPI
Subject
/ Drugs
/ GPCRs
/ Humans
/ Kinases
/ Ligands
/ Magnetic resonance spectroscopy
/ Molecular Docking Simulation
/ Nuclear magnetic resonance spectroscopy
/ Protein structure prediction
/ Proteins
/ Receptors, G-Protein-Coupled - chemistry
/ Receptors, G-Protein-Coupled - metabolism
/ Review
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