Reviewnpj drug discovery2025
AI meets physics in computational structure-based drug discovery for GPCRs.
Review in npj drug discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
15 citing papers in PubMed.
- On the generalization and usability of cofolding models for GPCR drug discovery.npj drug discovery · 2026Article
- New paradigm of q-CAR drug development targeting disease-specific protein conformations.npj drug discovery · 2026Review
- V-SYNTHES2-the next generation tool for structure-based virtual screening of giga-scale chemical spaces.npj drug discovery · 2026Article
- Computer-aided structural modeling and drug discovery for G-protein-coupled receptors in the age of artificial intelligence.Current opinion in structural biology · 2026Review
- Elucidating the Structure and Activation Mechanisms of GPCRs Using Modern Computational and AI Tools.Cell biochemistry and biophysics · 2026Review
- Engineered Exosomes: Innovative Strategies for Precision Drug Delivery in Parkinson's Disease.Molecular neurobiology · 2026Review
- Pharmacological proximities in the GPCR family discovered using contact-informed amino-acid and binding pocket similarities.bioRxiv : the preprint server for biology · 2026Article
- Identification of Subtype-Selective Binding Sites in the Opioid Receptor Family.Journal of chemical information and modeling · 2026Article
- Identification of potential MenT3 inhibitors for Mycobacterium tuberculosis using the generative artificial intelligence and SilicoXplore platform.Scientific reports · 2026Article
- Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery.Cell biochemistry and biophysics · 2026Review
- CS-DTA: a language model-driven framework for robust drug-target affinity prediction under strict cold-start scenarios.Frontiers in chemistry · 2026Article
- Optimization of next-generation CXCR4 ligands for radiotheranostic applications.Frontiers in pharmacology · 2026Review
- Large scale prospective evaluation of co-folding across 557 Mac1-ligand complexes and three virtual screens.bioRxiv : the preprint server for biology · 2025Article
- V-synthes2 - the Next Generation Tool for Structure-based Virtual Screening of Giga-scale Chemical Spaces.Research square · 2025Article
- Recent trends in machine learning and deep learning-based prediction of G-protein coupled receptor-ligand binding affinities.Frontiers in bioinformatics · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
G protein-coupled receptors (GPCRs) are a prominent class of therapeutic targets for which structure-based drug discovery (SBDD) has traditionally been challenging to apply. However, recent artificial intelligence (AI)-powered breakthroughs have opened new avenues. Here, we discuss the impact of computational models on hit discovery and lead optimization for GPCRs. We also provide best practices for generating and validating predictive models for prospective use.
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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.