ReviewMethods (San Diego, Calif.)2020
Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition.
Review in Methods (San Diego, Calif.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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
42 citing papers in PubMed.
- Article
- Elucidating the Structure and Activation Mechanisms of GPCRs Using Modern Computational and AI Tools.Cell biochemistry and biophysics · 2026Review
- Constructing and characterizing trillion-scale combinatorial chemical library.Journal of cheminformatics · 2026Article
- Discovering anti-obesity blue food compounds via combined deep learning and in silico approaches.Molecular diversity · 2026Article
- Identification of Neferine as a DOR Agonist Activating GInternational journal of molecular sciences · 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
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- Deep Learning Pipeline for Accelerating Virtual Screening in Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Variability vs. phenotype: Multimodal analysis of Dravet syndrome brain organoids powered by deep learning.iScience · 2025Article
- Prediction of bone or lung metastasis in prostate cancer using machine learning: a SEER-based study with external validation.Discover oncology · 2025Article
- Constructing a prediction model for delayed wound healing after gastric cancer radical surgery based on three machine learning algorithms.World journal of gastrointestinal oncology · 2025Article
- Machine learning models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1.Molecular diversity · 2025Article
- Molecular Modelling in Bioactive Peptide Discovery and Characterisation.Biomolecules · 2025Review
- Fine-Tuned Deep Transfer Learning Models for Large Screenings of Safer Drugs Targeting Class A GPCRs.Biochemistry · 2025Article
- Deep learning-guided discovery of selective JAK2-JH2 allosteric inhibitors: integration of MLP predictive modeling, BREED-based library design, and computational validation.Frontiers in chemistry · 2025Article
- Hybrid deep learning technique for COX-2 inhibition bioactivity detection against breast cancer disease.Biomedical engineering letters · 2024Article
- Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease.Cell reports · 2024Article
- Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.Current pharmaceutical design · 2024Review
- Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques.The journal of physical chemistry. B · 2023Article
- Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be attributed to the latest developments in deep learning. When applied to various scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep learning has been shown to outperform not only conventional machine learning but also highly specialized tools developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends. To make this article accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying methodology, including overviews of the most commonly used deep learning architectures and feature representations of molecular data. We highlight the latest AI-based research that has led to the successful discovery of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting the recent research trends in deep learning, such as active learning and semi-supervised learning, which have great potential for advancing bioactive ligand discovery.
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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.