Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, P. R. China.
State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, P. R. China.
Mol Cancer. 2024 Oct 5;23(1):223. doi: 10.1186/s12943-024-02140-6.
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 = 0.9480, p = 0.0051), "artificial intelligence" (s = 5.00, R = 0.8096, p = 0.0375), "drug discovery" (s = 1.90, R = 0.7987, p = 0.0409), and "molecular dynamics" (s = 2.40, R = 0.8000, p = 0.0405) as core hotspots driving the research frontier. More importantly, the Walktrap algorithm further reveals that "structure prediction, artificial intelligence, molecular dynamics" (Relevance Percentage[RP] = 100%, Development Percentage[DP] = 25.0%), "sars-cov-2, covid-19, vaccine design" (RP = 97.8%, DP = 37.5%), and "homology modeling, virtual screening, membrane protein" (RP = 89.9%, DP = 26.1%) are closely intertwined with the AlphaFold model but remain underexplored, which implies a broad exploration space. In conclusion, through the machine-learning-driven informatics methods, this scientometric analysis offers an objective and comprehensive overview of global AlphaFold research, identifying critical research clusters and hotspots while prospectively pointing out underexplored critical areas.
AlphaFold 模型改变了生物学研究。然而，整个 AlphaFold 领域中大量未结构化的数据需要进一步分析，以充分了解当前的研究现状并指导未来的探索。因此，本研究采用基于机器学习的信息学方法，旨在通过分析确定关键研究集群，跟踪新兴趋势，并突出该领域中尚未充分探索的领域。定量统计分析表明，AlphaFold 领域呈现出惊人的发展趋势（年增长率=180.13%）和全球合作（国际合著率=33.33%）。无监督聚类算法、时间序列跟踪和全球影响力评估指出，第 3 集群（人工智能推动 AlphaFold 在结构生物学中的进展）影响力最大（平均引用数=48.36±184.98）。此外，回归曲线和热点突发分析突出了“结构预测”（s=12.40，R=0.9480，p=0.0051）、“人工智能”（s=5.00，R=0.8096，p=0.0375）、“药物发现”（s=1.90，R=0.7987，p=0.0409）和“分子动力学”（s=2.40，R=0.8000，p=0.0405）是推动研究前沿的核心热点。更重要的是，Walktrap 算法进一步揭示了“结构预测、人工智能、分子动力学”（相关性百分比[RP]=100%，发展百分比[DP]=25.0%）、“sars-cov-2、covid-19、疫苗设计”（RP=97.8%，DP=37.5%）和“同源建模、虚拟筛选、膜蛋白”（RP=89.9%，DP=26.1%）与 AlphaFold 模型紧密相关但仍未得到充分探索，这意味着有广泛的探索空间。总之，通过基于机器学习的信息学方法，本研究对全球 AlphaFold 研究进行了客观、全面的概述，确定了关键研究集群和热点，同时前瞻性地指出了尚未充分探索的关键领域。
