ReviewInternational journal of molecular sciences2025
AlphaFold3: An Overview of Applications and Performance Insights.
Review in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 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
68 citing papers in PubMed.
- Non-coding small RNAs buffer protein interactions to prevent oncogenic aggregation: structural dampening of aberrant PPIs by RNA.RNA biology · 2026Article
- Article
- TechBio 3.0 closes the drug discovery loop with multimodal AI and generative chemistry.npj drug discovery · 2026Review
- Structural and Evolutionary Analysis of Non-specific Lipid Transfer Proteins (nsLTPs) in Cereus (Cactaceae).Journal of molecular evolution · 2026Article
- AntiLipo: A Comprehensive Database, Deep Learning-Based Prediction Model, and Computational Study of Anti-Hyperlipidemic Peptides.Interdisciplinary sciences, computational life sciences · 2026Article
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- Mechanism and Component Study of Scorpion Peptides Regulation of Macrophage Polarization in Diabetic Wounds.Biomedicines · 2026Article
- A chromosome-level genome assembly of Lycoris radiata reveals the evolutionary origin of Amaryllidaceae alkaloids and elucidates the complete galanthamine biosynthetic pathway.Plant communications · 2026Article
- Deep learning-based structural prediction (AlphaFold 3) of shrimp IMNV RdRp and identification of seaweed-derived metabolites for antiviral intervention in aquaculture.Virus research · 2026Article
- In silico conformational dynamics of the α-actinin-2 actin-binding domain upon phosphorylation.Biophysical journal · 2026Article
- Microbial lipases: advances in metagenomics and artificial intelligence for enzyme discovery and engineering.Archives of microbiology · 2026Review
- Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance.Journal of computer-aided molecular design · 2026Review
- AI-Assisted Spatial Metabolic Engineering in Plants: Integrating Flux Design, Spatial Omics, and Synthetic Biology.Metabolites · 2026Review
- Evaluation of AlphaFold3 for Predicting Human Heme-Binding Protein Structures.International journal of molecular sciences · 2026Article
- Integrated deep learning and multi-scale modeling for the discovery of pan-genotypic HCV NS5B polymerase inhibitors.Molecular diversity · 2026Article
- Benchmarking AI Protein Structure Predictors Reveals a Persistent Bias in Multi-State Proteins.bioRxiv : the preprint server for biology · 2026Article
- A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.Briefings in bioinformatics · 2026Review
- Broad and durable protection against SARS-CoV-2 and SARS-CoV by an intranasal chimpanzee adenovirus vaccine expressing tandem RBDs and nucleocapsid.PLoS pathogens · 2026Article
- Benchmarking Generative AI Protein Models Reveals Differences Between Structural and Sequence-based Approaches.Genomics, proteomics & bioinformatics · 2026Article
- Caveat emptor: predicting and modeling protein-DNA recognition and binding via machine-learning computational approaches.Nucleic acids research · 2026Review
8 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
8 authors.
Funding
Abstract
AlphaFold3, the latest release of AlphaFold developed by Google DeepMind and Isomorphic Labs, was designed to predict protein structures with remarkable accuracy. AlphaFold3 enhances our ability to model not only single protein structures but also complex biomolecular interactions, including protein-protein interactions, protein-ligand docking, and protein-nucleic acid complexes. Herein, we provide a detailed examination of AlphaFold3's capabilities, emphasizing its applications across diverse biological fields and its effectiveness in complex biological systems. The strengths of the new AI model are also highlighted, including its ability to predict protein structures in dynamic systems, multi-chain assemblies, and complicated biomolecular complexes that were previously challenging to depict. We explore its role in advancing drug discovery, epitope prediction, and the study of disease-related mutations. Despite its significant improvements, the present review also addresses ongoing obstacles, particularly in modeling disordered regions, alternative protein folds, and multi-state conformations. The limitations and future directions of AlphaFold3 are discussed as well, with an emphasis on its potential integration with experimental techniques to further refine predictions. Lastly, the work underscores the transformative contribution of the new model to computational biology, providing new insights into molecular interactions and revolutionizing the fields of accelerated drug design and genomic research.
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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.