ArticleBMC bioinformatics2022
A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 24 citations in OpenAlex.
- Dynamic-GLEP: a dynamics-informed deep learning framework for ligand efficacy prediction in representative Class A GPCRs.Briefings in bioinformatics · 2026Article
- GPCR-A17 MAAP: mapping modulators, agonists, and antagonists to predict the next bioactive target.Journal of cheminformatics · 2025Article
- EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network.BMC biology · 2025Article
- Ligand-Induced Biased Activation of GPCRs: Recent Advances and New Directions from In Silico Approaches.Molecules (Basel, Switzerland) · 2025Review
- AiGPro: a multi-tasks model for profiling of GPCRs for agonist and antagonist.Journal of cheminformatics · 2025Article
- Leveraging Artificial Intelligence in GPCR Activation Studies: Computational Prediction Methods as Key Drivers of Knowledge.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Enabling systemic identification and functionality profiling for Cdc42 homeostatic modulators.Communications chemistry · 2024Article
- G Protein-Coupled Receptor-Ligand Pose and Functional Class Prediction.International journal of molecular sciences · 2024Article
- Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities.International journal of molecular sciences · 2024Review
- Decrypting orphan GPCR drug discovery via multitask learning.Journal of cheminformatics · 2024Article
- Orphan G protein-coupled receptors: the ongoing search for a home.Frontiers in pharmacology · 2024Review
- Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques.The journal of physical chemistry. B · 2023Article
- High expression of GPR50 promotes the proliferation, migration and autophagy of hepatocellular carcinoma cells in vitro.Journal of cell communication and signaling · 2023Article
- Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes.Journal of chemical information and modeling · 2023Article
- Expression of olfactory receptor genes in non-olfactory tissues in the developing and adult zebrafish.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundG-protein coupled receptors (GPCRs) sense and transmit extracellular signals into the intracellular machinery by regulating G proteins. GPCR malfunctions are associated with a variety of signaling-related diseases, including cancer and diabetes; at least a third of the marketed drugs target GPCRs. Thus, characterization of their signaling and regulatory mechanisms is crucial for the development of effective drugs.
resultsIn this study, we developed a machine learning model to identify GPCR agonists and antagonists. We designed two-step prediction models: the first model identified the ligands binding to GPCRs and the second model classified the ligands as agonists or antagonists. Using 990 selected subset features from 5270 molecular descriptors calculated from 4590 ligands deposited in two drug databases, our model classified non-ligands, agonists, and antagonists of GPCRs, and achieved an area under the ROC curve (AUC) of 0.795, sensitivity of 0.716, specificity of 0.744, and accuracy of 0.733. In addition, we verified that 70% (44 out of 63) of FDA-approved GPCR-targeting drugs were correctly classified into their respective groups.
conclusionsStudies of ligand-GPCR interaction recognition are important for the characterization of drug action mechanisms. Our GPCR-ligand interaction prediction model can be employed in the pharmaceutical sciences for the efficient virtual screening of putative GPCR-binding agonists and antagonists.
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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.