ReviewStructure (London, England : 1993)2021
Open-access data: A cornerstone for artificial intelligence approaches to protein structure prediction.
Review in Structure (London, England : 1993), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Article
- Article
- Open-Source Molecular Docking and AI-Augmented Structure-Based Drug Design: Current Workflows, Challenges, and Opportunities.International journal of molecular sciences · 2026Review
- Usage and impact of global biodata resources.Bioinformatics advances · 2026Article
- Alternative Conformation Prediction Using Deep Learning With Multi-MSA Strategy and Structural Clustering in CASP16.Proteins · 2026Article
- Molecular Chain Elongation Mechanism for n-Caproate Biosynthesis by Megasphaera Hexanoica.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- How many crystal structures do you need to trust your docking results?bioRxiv : the preprint server for biology · 2025Article
- Protein-ligand data at scale to support machine learning.Nature reviews. Chemistry · 2025Review
- From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning.Structural dynamics (Melville, N.Y.) · 2025Article
- High-throughput prediction of peptide structural conformations with AlphaFold2.bioRxiv : the preprint server for biology · 2025Article
- Generative artificial intelligence performs rudimentary structural biology modeling.Scientific reports · 2024Article
- Review
- Generative artificial intelligence performs rudimentary structural biology modeling.bioRxiv : the preprint server for biology · 2024Article
- Article
- Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Protein structure prediction from the complementary science perspective.Biophysical reviews · 2023Article
- Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2023Article
- Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data.bioRxiv : the preprint server for biology · 2023Article
- RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning.Nucleic acids research · 2023Article
- Evaluation of AlphaFold structure-based protein stability prediction on missense variations in cancer.Frontiers in genetics · 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
The Protein Data Bank (PDB) was established in 1971 to archive three-dimensional (3D) structures of biological macromolecules as a public good. Fifty years later, the PDB is providing millions of data consumers around the world with open access to more than 175,000 experimentally determined structures of proteins and nucleic acids (DNA, RNA) and their complexes with one another and small-molecule ligands. PDB data users are working, teaching, and learning in fundamental biology, biomedicine, bioengineering, biotechnology, and energy sciences. They also represent the fields of agriculture, chemistry, physics and materials science, mathematics, statistics, computer science, and zoology, and even the social sciences. The enormous wealth of 3D structure data stored in the PDB has underpinned significant advances in our understanding of protein architecture, culminating in recent breakthroughs in protein structure prediction accelerated by artificial intelligence approaches and deep or machine learning methods.
Indexed as
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.