ReviewSignal transduction and targeted therapy2023
AlphaFold2 and its applications in the fields of biology and medicine.
Review in Signal transduction and targeted therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 268 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.
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Who cites it
268 citing papers in PubMed, 513 citations in OpenAlex.
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- Towards Cost-Effective and Sustainable Media Formulations for Terrestrial and Aquatic Cellular Agriculture.Foods (Basel, Switzerland) · 2026Review
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- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Distribution and Evolutionary Implications of Flagellum-Associated Gene Families in Representative Algal Genomes.Biology · 2026Article
- PAMP orchestrates proline metabolic rewiring to suppress LUAD via PYCR1 inhibition.EMBO molecular medicine · 2026Article
- Targeting the Undruggable: Deep Learning-Driven Design of Peptide Therapeutics in Cancer.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Phage-Encoded Depolymerase DepKP144 with Therapeutic Potential Against Both K1- and K2-TypeInternational journal of molecular sciences · 2026Article
- Review
- UniPTMs: a unified multi-type PTM site prediction model via master-slave architecture-based multi-stage fusion strategy and hierarchical contrastive loss.BMC bioinformatics · 2026Article
- From Berry to Bedside: Translational Potential of Berry-Derived Phytochemicals in HNSCC.Molecules (Basel, Switzerland) · 2026Review
- Decoding the functional network of circular RNAs encoding proteins in hepatocellular carcinoma: from carcinogenesis to clinical transformation.Journal of advanced research · 2026Review
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Ten quick tips for classifying unknown bacteriophage.PLoS computational biology · 2026Article
- Deep learning-driven decoding of ubiquitination: from regulatory mechanisms to targeted protein degradation.Biology direct · 2026Review
- In Silico Design and Computational Characterization of Novel Chimeric Multiepitope Antigens for Mpox Serosurveillance: An Immunoinformatics Approach.Health science reports · 2026Article
- Succinic semialdehyde dehydrogenase deficiency: exploring the relationship between ALDH5A1 variants and molecular effect on SSADH.Orphanet journal of rare diseases · 2026Article
- Extending structural surfaceomics to identify aberrant conformations of tumor surface proteins as potential immunotherapy targets.bioRxiv : the preprint server for biology · 2026Article
208 more citing papers are in PubMed but not listed here.
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 at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.
Indexed as
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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.