Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation.
AlphaFold
Artificial intelligence
Bibliometrics
Drug discovery
Molecular dynamics
Structure prediction
Journal
Molecular cancer
ISSN: 1476-4598
Titre abrégé: Mol Cancer
Pays: England
ID NLM: 101147698
Informations de publication
Date de publication:
05 Oct 2024
05 Oct 2024
Historique:
received:
03
09
2024
accepted:
30
09
2024
medline:
6
10
2024
pubmed:
6
10
2024
entrez:
5
10
2024
Statut:
epublish
Résumé
AlphaFold model has reshaped biological research. However, vast unstructured data in the entire AlphaFold field requires further analysis to fully understand the current research landscape and guide future exploration. Thus, this scientometric analysis aimed to identify critical research clusters, track emerging trends, and highlight underexplored areas in this field by utilizing machine-learning-driven informatics methods. Quantitative statistical analysis reveals that the AlphaFold field is enjoying an astonishing development trend (Annual Growth Rate = 180.13%) and global collaboration (International Co-authorship = 33.33%). Unsupervised clustering algorithm, time series tracking, and global impact assessment point out that Cluster 3 (Artificial Intelligence-Powered Advancements in AlphaFold for Structural Biology) has the greatest influence (Average Citation = 48.36 ± 184.98). Additionally, regression curve and hotspot burst analysis highlight "structure prediction" (s = 12.40, R
Identifiants
pubmed: 39369244
doi: 10.1186/s12943-024-02140-6
pii: 10.1186/s12943-024-02140-6
doi:
Types de publication
Journal Article
Letter
Langues
eng
Sous-ensembles de citation
IM
Pagination
223Subventions
Organisme : National Natural Science Foundation of China
ID : 82422010
Organisme : Guangdong Basic and Applied Basic Research Foundation
ID : 2024B1515020026
Informations de copyright
© 2024. The Author(s).
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