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, R2 = 0.9480, p = 0.0051), “artificial intelligence” (s = 5.00, R2 = 0.8096, p = 0.0375), “drug discovery” (s = 1.90, R2 = 0.7987, p = 0.0409), and “molecular dynamics” (s = 2.40, R2 = 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.

中文翻译：

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用于分子生物学和药物发现的人工智能 alphafold 模型：机器学习驱动的信息学研究


AlphaFold 模型重塑了生物学研究。然而，整个 AlphaFold 领域的大量非结构化数据需要进一步分析，以充分了解当前的研究前景并指导未来的探索。因此，这项科学计量分析旨在通过利用机器学习驱动的信息学方法确定关键的研究集群，跟踪新兴趋势，并突出该领域未开发的领域。定量统计分析表明，AlphaFold 领域正处于惊人的发展趋势（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评估指出，聚类 3 （Artificial Intelligence-Powered Advancements in AlphaFold for Structural Biology） 的影响力最大 （平均引用次数 = 48.36 ± 184.98）。此外，回归曲线和热点爆发分析突出了“结构预测”（s = 12.40，R2 = 0.9480，p = 0.0051）、“人工智能”（s = 5.00，R2 = 0.8096，p = 0.0375）、“药物发现”（s = 1.90，R2 = 0.7987，p = 0.0409）和“分子动力学”（s = 2.40，R2 = 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%，开发百分比 [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 模型紧密交织在一起，但仍未得到充分探索，这意味着广阔的探索空间。 总之，通过机器学习驱动的信息学方法，这种科学计量分析提供了全球 AlphaFold 研究的客观和全面概述，确定了关键的研究集群和热点，同时前瞻性地指出了未充分开发的关键领域。