The application of artificial intelligence to accelerate G protein‐coupled receptor drug discovery

Nguyen, Thi Ngoc Anh; Nguyen, Diep Thi Ngoc; Koh, Huan Yee; Jason, Toskov,; MacLean, William; Xu, Andrew Wei; Zhang, Chengqi; Webb, Geoffrey I.; May, Lauren T.; Halls, Michelle L.

doi:10.1111/bph.16140

Cited by 12 publications

(5 citation statements)

References 118 publications

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“…Structure-based virtual screening has been widely utilised to discover active molecules targeting various therapeutic targets [ 84 ]. Chemical and protein datasets with rich bioactivity data have been obtained [ 85 ]. Artificial intelligence, especially machine learning methods, including deep learning, has successfully utilised these datasets for the construction of score functions necessary for the virtual screening of targets with information about three-dimensional atomic structure [ 86 ].…”

Section: Gpcrs Help In the Discovery Of New Targeted Tcm Or Drugsmentioning

confidence: 99%

G protein-coupled receptors and traditional Chinese medicine: new thinks for the development of traditional Chinese medicine

Zhang,

An,

You

et al. 2024

Chin Med

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G protein-coupled receptors (GPCRs) widely exist in vivo and participate in many physiological processes, thus emerging as important targets for drug development. Approximately 30% of the Food and Drug Administration (FDA)-approved drugs target GPCRs. To date, the ‘one disease, one target, one molecule’ strategy no longer meets the demands of drug development. Meanwhile, small-molecule drugs account for 60% of FDA-approved drugs. Traditional Chinese medicine (TCM) has garnered widespread attention for its unique theoretical system and treatment methods. TCM involves multiple components, targets and pathways. Centered on GPCRs and TCM, this paper discusses the similarities and differences between TCM and GPCRs from the perspectives of syndrome of TCM, the consistency of TCM’s multi-component and multi-target approaches and the potential of GPCRs and TCM in the development of novel drugs. A novel strategy, ‘simultaneous screening of drugs and targets’, was proposed and applied to the study of GPCRs. We combine GPCRs with TCM to facilitate the modernisation of TCM, provide valuable insights into the rational application of TCM and facilitate the research and development of novel drugs. This study offers theoretical support for the modernisation of TCM and introduces novel ideas for development of safe and effective drugs.

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Section: Gpcrs Help In the Discovery Of New Targeted Tcm Or Drugsmentioning

confidence: 99%

G protein-coupled receptors and traditional Chinese medicine: new thinks for the development of traditional Chinese medicine

Zhang,

An,

You

et al. 2024

Chin Med

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“…As we navigate through this exciting era of GPCR research, artificial intelligence (AI) stands out as an invaluable tool, playing a promising role in elucidating GPCR structures and facilitating the discovery and development of novel drugs [ 362 ]. AI computational capabilities allow for the efficient integration and analysis of vast and complex datasets, aiding in the identification of potential therapeutic targets and the optimization of lead compounds for GPCRs [ 363 ].…”

Section: Future Opportunities and Summarymentioning

confidence: 99%

Structure, function and drug discovery of GPCR signaling

Cheng,

Xia,

et al. 2023

Mol Biomed

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G protein-coupled receptors (GPCRs) are versatile and vital proteins involved in a wide array of physiological processes and responses, such as sensory perception (e.g., vision, taste, and smell), immune response, hormone regulation, and neurotransmission. Their diverse and essential roles in the body make them a significant focus for pharmaceutical research and drug development. Currently, approximately 35% of marketed drugs directly target GPCRs, underscoring their prominence as therapeutic targets. Recent advances in structural biology have substantially deepened our understanding of GPCR activation mechanisms and interactions with G-protein and arrestin signaling pathways. This review offers an in-depth exploration of both traditional and recent methods in GPCR structure analysis. It presents structure-based insights into ligand recognition and receptor activation mechanisms and delves deeper into the mechanisms of canonical and noncanonical signaling pathways downstream of GPCRs. Furthermore, it highlights recent advancements in GPCR-related drug discovery and development. Particular emphasis is placed on GPCR selective drugs, allosteric and biased signaling, polyphamarcology, and antibody drugs. Our goal is to provide researchers with a thorough and updated understanding of GPCR structure determination, signaling pathway investigation, and drug development. This foundation aims to propel forward-thinking therapeutic approaches that target GPCRs, drawing upon the latest insights into GPCR ligand selectivity, activation, and biased signaling mechanisms.

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“…The accurate calculation of the residence time poses challenges with conventional MDs alone due to the extensive sampling required. Several promising enhanced sampling ,− and ML methods have been developed for computing ligand–receptor binding kinetics. Studies have been performed on GPCRs, e.g., in hA 2A R, human β 2 AR, , muscarinic receptor M 3 (M 3 R), muscarinic acetylcholine receptor M 2 (mAChR M 2 ), mAChR M 3 , and corticotropin-releasing factor type 1 receptor (CRF 1 R) .…”

Section: Introductionmentioning

confidence: 99%

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A₃ Receptor

Stampelou,

Ladds,

Kolocouris

2024

J. Phys. Chem. B

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A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A 3 receptor (hA 3 R). We tested three available homology models from which two have been generated from experimental structures of hA 1 R or hA 2 AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol −1 , the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R173 5.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol −1 ). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

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The application of artificial intelligence to accelerate G protein‐coupled receptor drug discovery

Cited by 12 publications

References 118 publications

G protein-coupled receptors and traditional Chinese medicine: new thinks for the development of traditional Chinese medicine

G protein-coupled receptors and traditional Chinese medicine: new thinks for the development of traditional Chinese medicine

Structure, function and drug discovery of GPCR signaling

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A₃ Receptor

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The application of artificial intelligence to accelerate G protein‐coupled receptor drug discovery

Cited by 12 publications

References 118 publications

G protein-coupled receptors and traditional Chinese medicine: new thinks for the development of traditional Chinese medicine

G protein-coupled receptors and traditional Chinese medicine: new thinks for the development of traditional Chinese medicine

Structure, function and drug discovery of GPCR signaling

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

Contact Info

Product

Resources

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Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A₃ Receptor