ArticleChemistryOpen2025
Discovery of Potential GPRC5D Inhibitors through Virtual Screening and Molecular Dynamics Simulations.
Article in ChemistryOpen, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Deep learning-guided high-throughput screening and molecular dynamics simulations facilitate the discovery of potential FXIIa inhibitors.Journal of molecular modeling · 2026Article
- Discovery of Potential GPRC5D Inhibitors through Virtual Screening and Molecular Dynamics Simulations.ChemistryOpen · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
G protein-coupled receptor family C, group 5, member D (GPRC5D), a member of the G protein-coupled receptor (GPCR) family, has recently emerged as a promising target for immunotherapy in hematologic malignancies, particularly multiple myeloma. However, no systematic virtual screening studies have been conducted to identify small-molecule inhibitors targeting GPRC5D. To address this gap, a multistep computational screening strategy is developed that integrates Protein-Ligand Affinity prediction NETwork (PLANET), a GPU-accelerated version of AutoDock Vina (Vina-GPU), molecular mechanics/generalized born surface area (MM/GBSA), and an online tool for Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) property prediction (admetSAR 3.0), complemented by molecular dynamics (MD) simulations and absolute binding free energy (ABFE). From an initial library of 8,617 compounds, four candidates (compounds 1, 2, 7, and 8) are prioritized. Among them, compound 2 shows relatively strong binding affinity (MM/GBSA ΔG = -79.8 kcal mol
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Identifiers
What OpenQuestion holds
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.