ReviewNature biotechnology2024
Computational drug development for membrane protein targets.
Review in Nature biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
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.
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Who cites it
26 citing papers in PubMed, 50 citations in OpenAlex.
- Over-expression, purification, and kinetic analysis ofJournal of enzyme inhibition and medicinal chemistry · 2026Article
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- Mifepristone ameliorates osteoporosis & hyperlipidemia comorbidity through SPNS3-S1P-S1PR1 axis via regulating BMSCs differentiation.Journal of pharmaceutical analysis · 2026Article
- AET5: A transcriptome-guided molecular generation framework with contrastive self-supervised learning.PLoS computational biology · 2026Article
- Assessing Surface Binding Events on Golgi Microsomes Isolated from Cancer Cells by Capillary Electrophoresis.Analytical chemistry · 2026Article
- The serotonin-gated 5-HT3 receptor: a tale of functions and structures of a prototypical pentameric ligand-gated ion channel.Acta pharmacologica Sinica · 2026Review
- A Comparative Review of Artificial Intelligence Applications in Small Molecule Versus Peptide Drug Discovery.International journal of molecular sciences · 2026Review
- Rethinking Nature's Pharmacy: AI Era and Natural Product Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Targeting ion channel dysregulation in tumors: emerging therapeutic opportunities.Trends in pharmacological sciences · 2026Review
- Machine learning and bioinformatics identify and validate membrane protein biomarkers for type 2 diabetes mellitus.Frontiers in endocrinology · 2026Article
- Discovery of the PARP1 Inhibitors from Natural Compounds Using Structure-Based Virtual Screening and Bioactivity Evaluation.Medicinal chemistry (Shariqah (United Arab Emirates)) · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Graph neural networks driven acceleration in drug discovery.Acta pharmaceutica Sinica. B · 2025Review
- A novel DCSTAMP antagonist impedes preosteoclast fusion via modulation of RAP1B-RAC1-mediated cytoskeletal remodeling.Experimental & molecular medicine · 2025Article
- Advances in native cell membrane nanoparticles system.Current opinion in structural biology · 2025Review
- Post-Translational Modifications in Cilia and Ciliopathies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- RUNX1/SLAMF3 Axis Drives Immunosuppression to Contribute to Colorectal Cancer Liver Metastasis by Blocking Phagocytosis and Depleting C1QCAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Exploring Conformational Transitions in Biased and Balanced Ligand Binding of GLP-1R.Molecules (Basel, Switzerland) · 2025Article
- Deep Learning-Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Biomembrane structure at the molecular level and its application in precision medicine.Biophysics reviews · 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
12 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.
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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.