ArticleInternational journal of molecular sciences2021
Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Peptide-based therapeutics targeting GPCRs: recent applications in the treatment of metabolic disorders.Frontiers in pharmacology · 2026Review
- GPCR Sense Communication Among Interaction Nematodes with Other Organisms.International journal of molecular sciences · 2025Review
- GPCRVS - AI-driven Decision Support System for GPCR Virtual Screening.International journal of molecular sciences · 2025Article
- Keras/TensorFlow in Drug Design for Immunity Disorders.International journal of molecular sciences · 2023Article
- Chemokine Receptors-Structure-Based Virtual Screening Assisted by Machine Learning.Pharmaceutics · 2023Article
- Feasibility and application of machine learning enabled fast screening of poly-beta-amino-esters for cartilage therapies.Scientific reports · 2022Article
- The Nonpeptide Agonist MK-5046 Functions As an Allosteric Agonist for the Bombesin Receptor Subtype-3.The Journal of pharmacology and experimental therapeutics · 2022Article
- Drug Repositioning For Allosteric Modulation of VIP and PACAP Receptors.Frontiers in endocrinology · 2021Article
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Authors and funding
2 authors.
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Abstract
The large amount of data that has been collected so far for G protein-coupled receptors requires machine learning (ML) approaches to fully exploit its potential. Our previous ML model based on gradient boosting used for prediction of drug affinity and selectivity for a receptor subtype was compared with explicit information on ligand-receptor interactions from induced-fit docking. Both methods have proved their usefulness in drug response predictions. Yet, their successful combination still requires allosteric/orthosteric assignment of ligands from datasets. Our ligand datasets included activities of two members of the secretin receptor family: GCGR and GLP-1R. Simultaneous activation of two or three receptors of this family by dual or triple agonists is not a typical kind of information included in compound databases. A precise allosteric/orthosteric ligand assignment requires a continuous update based on new structural and biological data. This data incompleteness remains the main obstacle for current ML methods applied to class B GPCR drug discovery. Even so, for these two class B receptors, our ligand-based ML model demonstrated high accuracy (5-fold cross-validation Q
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Registered trials
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