ArticlePharmacology research & perspectives2022
Combined docking and machine learning identify key molecular determinants of ligand pharmacological activity on β2 adrenoceptor.
Article in Pharmacology research & perspectives, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Two faces of drug repurposing data in machine learning-driven structure-based virtual screening supported by interaction fingerprints.Journal of computer-aided molecular design · 2026Article
- A select inhibitor of MORC2 encapsulated by chimeric membranecoated DNA nanocage target alleviation TNBC progression.Materials today. Bio · 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
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.Biomolecules · 2024Review
- Mechanisms and technologies in cancer epigenetics.Frontiers in oncology · 2024Review
- AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology.Chemical science · 2023Article
- Activity Map and Transition Pathways of G Protein-Coupled Receptor Revealed by Machine Learning.Journal of chemical information and modeling · 2023Article
- Combined docking and machine learning identify key molecular determinants of ligand pharmacological activity on β2 adrenoceptor.Pharmacology research & perspectives · 2022Article
Corrections and comments
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Authors and funding
14 authors at 1 institution in 2 countries.
Funding
Abstract
G protein-coupled receptors (GPCRs) are valuable therapeutic targets for many diseases. A central question of GPCR drug discovery is to understand what determines the agonism or antagonism of ligands that bind them. Ligands exert their action via the interactions in the ligand binding pocket. We hypothesized that there is a common set of receptor interactions made by ligands of diverse structures that mediate their action and that among a large dataset of different ligands, the functionally important interactions will be over-represented. We computationally docked ~2700 known β2AR ligands to multiple β2AR structures, generating ca 75 000 docking poses and predicted all atomic interactions between the receptor and the ligand. We used machine learning (ML) techniques to identify specific interactions that correlate with the agonist or antagonist activity of these ligands. We demonstrate with the application of ML methods that it is possible to identify the key interactions associated with agonism or antagonism of ligands. The most representative interactions for agonist ligands involve K97
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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.