Purpose To provide a comprehensive overview of research in AI-driven anticancer drug design and to recommend future research directions for both researchers and clinicians.
Methods A thorough literature search was conducted across the Web of Science Core Collection databases. Analysis was conducted using Excel 2025, CiteSpace, VOSviewer, and R.
Results This bibliometric analysis encompassed a total of 15,554 publications. China emerged as the leading contributor in terms of total publication output, followed by the United States, India, and South Korea. Harvard University produced the highest volume of publications. Professor Alex Zhavoronkov was identified as the most prolific author in this domain, while Professor Michael Patrick Menden received the highest number of citations. The terms “artificial intelligence,” “immunotherapy,” and “breast cancer” were the most frequently mentioned, with significant attention given to breast, prostate, lung, and liver cancers.
Conclusion AI-driven anticancer drug design emphasizes both established and novel targets, particularly for high-prevalence cancers. To enhance translational impact, future initiatives should focus on clinically oriented innovations. Key priorities must include the integration of multi-omics data, development of specialized cohorts, improved accessibility to data, and the combination of wet and dry lab tests. It will also be crucial to address data compliance and regulatory challenges to ensure sustained progress in this field.
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Funding
Financial Support for Cultural and Creative Industry Development Projects in Shanghai(2024360267)
Shanghai Leading Talent Program of Eastern Talent Plan(QNZH2024092)
RIGHTS & PERMISSIONS
The Author(s)
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