ArticleInternational journal of molecular sciences2021
Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 19 citations in OpenAlex.
- GC-MS and GC-IMS Comprehensive Analysis of Volatile Compounds in the Peel and Pulp of Six Lemon Varieties and Their Interactions with Olfactory Receptors: Molecular Docking and Molecular Dynamics Simulations Studies.Foods (Basel, Switzerland) · 2026Article
- The Emerging Role of Olfactory Receptors: From Genomics to Precision Medicine.Molecular diagnosis & therapy · 2026Review
- Roles of sensory receptors in non-sensory organs: the kidney and beyond.Nature reviews. Nephrology · 2025Review
- Classification of Odor-Derived Electroantennograms with Machine Learning.Integrative organismal biology (Oxford, England) · 2025Article
- Article
- M2OR: a database of olfactory receptor-odorant pairs for understanding the molecular mechanisms of olfaction.Nucleic acids research · 2024Article
- A Random Forest Model for Peptide Classification Based on Virtual Docking Data.International journal of molecular sciences · 2023Article
- Progress of the "Molecular Informatics" Section in 2022.International journal of molecular sciences · 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
- Editorial of Special Issue "Deep Learning and Machine Learning in Bioinformatics".International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Olfactory receptors (ORs) constitute the largest superfamily of G protein-coupled receptors (GPCRs). ORs are involved in sensing odorants as well as in other ectopic roles in non-nasal tissues. Matching of an enormous number of the olfactory stimulation repertoire to its counterpart OR through machine learning (ML) will enable understanding of olfactory system, receptor characterization, and exploitation of their therapeutic potential. In the current study, we have selected two broadly tuned ectopic human OR proteins, OR1A1 and OR2W1, for expanding their known chemical space by using molecular descriptors. We present a scheme for selecting the optimal features required to train an ML-based model, based on which we selected the random forest (RF) as the best performer. High activity agonist prediction involved screening five databases comprising ~23 M compounds, using the trained RF classifier. To evaluate the effectiveness of the machine learning based virtual screening and check receptor binding site compatibility, we used docking of the top target ligands to carefully develop receptor model structures. Finally, experimental validation of selected compounds with significant docking scores through in vitro assays revealed two high activity novel agonists for OR1A1 and one for OR2W1.
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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.