ArticlePLoS computational biology2021
Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 39 citations in OpenAlex.
- Natural chlocarbazomycins as potential adenosine A1 receptor antagonists: ligand-based and structure-based virtual screening, quantum chemical analysis and CNS MPO study.Molecular diversity · 2026Article
- Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.npj drug discovery · 2025Review
- Harnessing artificial intelligence for brain disease: advances in diagnosis, drug discovery, and closed-loop therapeutics.Frontiers in neurology · 2025Review
- Molecular and Structural Insight into Adenosine AMolecular neurobiology · 2023Review
- AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology.Chemical science · 2023Article
- Artificial Intelligence, Machine Learning, and Big Data for Ebola Virus Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Small molecule allosteric modulation of the adenosine AFrontiers in endocrinology · 2023Review
- Mind the Gap-Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Molecular Simulations and Drug Discovery of Adenosine Receptors.Molecules (Basel, Switzerland) · 2022Review
- Machine Learning for Discovery of New ADORA Modulators.Frontiers in pharmacology · 2022Article
- Identification of Novel Antagonists Targeting Cannabinoid Receptor 2 Using a Multi-Step Virtual Screening Strategy.Molecules (Basel, Switzerland) · 2021Article
- Current Approaches and Tools Used in Drug Development against Parkinson's Disease.Biomolecules · 2021Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adenosine receptors (ARs) have been demonstrated to be potential therapeutic targets against Parkinson's disease (PD). In the present study, we describe a multistage virtual screening approach that identifies dual adenosine A1 and A2A receptor antagonists using deep learning, pharmacophore models, and molecular docking methods. Nineteen hits from the ChemDiv library containing 1,178,506 compounds were selected and further tested by in vitro assays (cAMP functional assay and radioligand binding assay); of these hits, two compounds (C8 and C9) with 1,2,4-triazole scaffolds possessing the most potent binding affinity and antagonistic activity for A1/A2A ARs at the nanomolar level (pKi of 7.16-7.49 and pIC50 of 6.31-6.78) were identified. Further molecular dynamics (MD) simulations suggested similarly strong binding interactions of the complexes between the A1/A2A ARs and two compounds (C8 and C9). Notably, the 1,2,4-triazole derivatives (compounds C8 and C9) were identified as the most potent dual A1/A2A AR antagonists in our study and could serve as a basis for further development. The effective multistage screening approach developed in this study can be utilized to identify potent ligands for other drug targets.
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