Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2025 May 19;31(6):163.
doi: 10.1007/s00894-025-06392-x.

Unveiling the influence of fastest nobel prize winner discovery: alphafold's algorithmic intelligence in medical sciences

Niki Najar Najafi  1 , Reyhaneh Karbassian  1 , Helia Hajihassani  1 , Maryam Azimzadeh Irani  2
Affiliations
Review

Unveiling the influence of fastest nobel prize winner discovery: alphafold's algorithmic intelligence in medical sciences

Niki Najar Najafi et al. J Mol Model. .

Abstract

Context: AlphaFold's advanced AI technology has transformed protein structure interpretation. By predicting three-dimensional protein structures from amino acid sequences, AlphaFold has solved the complex protein-folding problem, previously challenging for experimental methods due to numerous possible conformations. Since its inception, AlphaFold has introduced several versions, including AlphaFold2, AlphaFold DB, AlphaFold Multimer, Alpha Missense, and AlphaFold3, each further enhancing protein structure prediction. Remarkably, AlphaFold is recognized as the fastest Nobel Prize winner in science history. This technology has extensive applications, potentially transforming treatment and diagnosis in medical sciences by reducing drug design costs and time, while elucidating structural pathways of human body systems. Numerous studies have demonstrated how AlphaFold aids in understanding health conditions by providing critical information about protein mutations, abnormal protein-protein interactions, and changes in protein dynamics. Researchers have also developed new technologies and pipelines using different versions of AlphaFold to amplify its potential. However, addressing existing limitations is crucial to maximizing AlphaFold's capacity to redefine medical research. This article reviews AlphaFold's impact on five key aspects of medical sciences: protein mutation, protein-protein interaction, molecular dynamics, drug design, and immunotherapy.

Methods: This review examines the contributions of various AlphaFold versions AlphaFold2, AlphaFold DB, AlphaFold Multimer, Alpha Missense, and AlphaFold3 to protein structure prediction. The methods include an extensive analysis of computational techniques and software used in interpreting and predicting protein structures, emphasizing advances in AI technology and its applications in medical research.

Keywords: AlphaFold; AlphaFold’s predictive capabilities; Case studies; Medicine; Protein structure prediction.

PubMed Disclaimer

Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Similar articles

See all similar articles

References

    1. Morris R, Black KA, Stollar EJ (2022) “Uncovering protein function: from classification to complexes,” (in eng). Essays Biochem 66(3):255–285. https://doi.org/10.1042/ebc20200108 - DOI - PubMed - PMC
    1. Heaven WD (2020) DeepMind’s protein-folding AI has solved a 50-year-old grand challenge of biology. MIT Technol Rev
    1. Maveyraud L, Mourey L (2020) Protein X-ray crystallography and drug discovery. Molecules 25(5):1030 - PubMed - PMC - DOI
    1. Benjin X, Ling L (2020) Developments, applications, and prospects of cryo-electron microscopy. Protein Sci 29(4):872–882 - PubMed - DOI
    1. Fowler NJ, Williamson MP (2022) The accuracy of protein structures in solution determined by AlphaFold and NMR. Structure 30(7):925–933 - PubMed - PMC - DOI

LinkOut - more resources