ArticleThe journal of physical chemistry letters2018
Agonists of G-Protein-Coupled Odorant Receptors Are Predicted from Chemical Features.
Article in The journal of physical chemistry letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- GPCR-A17 MAAP: mapping modulators, agonists, and antagonists to predict the next bioactive target.Journal of cheminformatics · 2025Article
- Combining Machine Learning and Electrophysiology for Insect Odorant Receptor Studies.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Leveraging Artificial Intelligence in GPCR Activation Studies: Computational Prediction Methods as Key Drivers of Knowledge.Methods in molecular biology (Clifton, N.J.) · 2025Article
- New insights into the roles of olfactory receptors in cardiovascular disease.Molecular and cellular biochemistry · 2024Review
- M2OR: a database of olfactory receptor-odorant pairs for understanding the molecular mechanisms of olfaction.Nucleic acids research · 2024Article
- Deciphering olfactory receptor binding mechanisms: a structural and dynamic perspective on olfactory receptors.Frontiers in molecular biosciences · 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
- A Two-Step Transcriptome Analysis of the Human Heart Reveals Broad and Disease-Responsive Expression of Ectopic Olfactory Receptors.International journal of molecular sciences · 2023Article
- Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes.Journal of chemical information and modeling · 2023Article
- Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1.Journal of chemical information and modeling · 2023Article
- Olfactory Receptors as an Emerging Chemical Sensing Scaffold.Biochemistry · 2023Review
- A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists.BMC bioinformatics · 2022Article
- Large-Scale G Protein-Coupled Olfactory Receptor-Ligand Pairing.ACS central science · 2022Article
- Omics Studies for the Identification of Ascidian Peptides, Cognate Receptors, and Their Relevant Roles in Ovarian Follicular Development.Frontiers in endocrinology · 2022Review
- Deconstructing the transmembrane core of class A G protein-coupled receptors.Trends in biochemical sciences · 2021Review
- Article
- Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors.International journal of molecular sciences · 2021Article
- The role of the odorant receptors in the formation of the sensory map.BMC biology · 2021Review
- Bitter Taste and Olfactory Receptors: Beyond Chemical Sensing in the Tongue and the Nose.The Journal of membrane biology · 2021Article
- Implications of the simple chemical structure of the odorant molecules interacting with the olfactory receptor 1A1.Genomics & informatics · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Predicting the activity of chemicals for a given odorant receptor is a longstanding challenge. Here the activity of 258 chemicals on the human G-protein-coupled odorant receptor (OR)51E1, also known as prostate-specific G-protein-coupled receptor 2 (PSGR2), was virtually screened by machine learning using 4884 chemical descriptors as input. A systematic control by functional in vitro assays revealed that a support vector machine algorithm accurately predicted the activity of a screened library. It allowed us to identify two novel agonists in vitro for OR51E1. The transferability of the protocol was assessed on OR1A1, OR2W1, and MOR256-3 odorant receptors, and, in each case, novel agonists were identified with a hit rate of 39-50%. We further show how ligands' efficacy is encoded into residues within OR51E1 cavity using a molecular modeling protocol. Our approach allows widening the chemical spaces associated with odorant receptors. This machine-learning protocol based on chemical features thus represents an efficient tool for screening ligands for G-protein-coupled odorant receptors that modulate non-olfactory functions or, upon combinatorial activation, give rise to our sense of smell.
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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.