ReviewFrontiers in artificial intelligence2026
The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.
Review in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine.Microorganisms · 2026Review
- Protein-protein interactions as therapeutic targets in breast cancer: integrated computational and experimental advances, mechanisms and clinical translation.Molecular biology reports · 2026Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The AlphaFold (AF) initiative profoundly impacted structural biology, evidenced by its 2024 Nobel Prize. AlphaFold progressed from AF1 to AF2, which achieved near-experimental accuracy in single-chain protein folding, and then to AF3, expanding predictions to protein-ligand, protein-nucleic acid, and protein-protein complexes. This evolution led to the widespread adoption of AF tools, expanded structural coverage, and greater accessibility through the AlphaFold Database (AFDB), accelerating translational research, especially in structure-based drug discovery (SBDD) and the study of complex macromolecular assemblies. AF1 uses deep neural networks (DNNs), AF2 employs the Evoformer to model evolutionarily related sequences, and AF3 applies the Pairformer for pairwise amino acid interactions. The main differences between AF versions are architectural. Remaining challenges include predicting protein dynamics and multiple conformational states. This review first outlines AlphaFold's architectural evolution, then explores the post-AlphaFold landscape and its global impact, discusses translational research applications, and addresses limitations and future directions. Despite challenges, AlphaFold is poised to further advance structural biology, particularly in biotechnology and medicine.
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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.