ArticleJournal of cheminformatics2025
AiGPro: a multi-tasks model for profiling of GPCRs for agonist and antagonist.
Article in Journal of cheminformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings.Molecular cell · 2026Article
- Evaluation of deep learning architectures for predicting ligand interactions with neurologically relevant GPCRs.Journal of cheminformatics · 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
- Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery.Cell biochemistry and biophysics · 2026Review
- A Machine Learning-Guided Approach for Identifying Potential HCAR1 Antagonists in Lactate-Driven Cancers.ACS omega · 2026Article
- BOLD-GPCRs: A Transformer-Powered App for Predicting Ligand Bioactivity and Mutational Effects across Class A GPCRs.Journal of chemical information and modeling · 2026Article
- GPCRact: a hierarchical framework for predicting ligand-induced GPCR activity via allosteric communication modeling.Briefings in bioinformatics · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening. To address these issues, we introduce AiGPro, a novel multitask model designed to predict small molecule agonists (EC
Indexed as
Identifiers
What Socratic 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 Socratic graph.