Mapping the Landscape of Drug Target Identification Based on Activity-Based Protein Profiling: A Bibliometric Analysis and Systematic Review
Year: 2026
Type: article
Study design: Systematic Review
Abstract: INTRODUCTION: Activity-Based Protein Profiling (ABPP) has been widely applied in the field of drug target identification by virtue of its unique technical advantages. To gain insights into the global research status of this field, this study outlines the research content and progress and predicts future development trends through bibliometric analysis. METHODS: This study adopted a dual-platform data retrieval strategy and conducted visual analysis by combining multidimensional metrics with the use of CiteSpace and VOSviewer. RESULTS: This study included a total of 5,326 relevant pieces of literature published between 2006 and 2025. The global number of publications showed an overall upward trend, particularly since 2019. The United States and China were the major contributing countries, while the Chinese Academy of Sciences and Benjamin Cravatt were the most active institutions and authors in this field, respectively. Analysis indicated that the Journal of Biological Chemistry made significant contributions to this field, and the keywords "protein" and "expression" had the highest frequency of occurrence. DISCUSSION: Research in this field focuses on technological innovation and clinical translation and exhibits a trend of interdisciplinary integration with artificial intelligence, spatial omics, and other disciplines. Future priorities will focus on the optimization of probe design, deepening international cooperation, and promoting the application of clinical translation. CONCLUSIONS: This study identifies that the current research focus lies in the development of key pathogenic protein inhibitors, the exploration of drug repurposing strategies, and the target validation of Chinese medicine monomers and natural products. This study provides valuable references for scholars in this field.
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7139950152
FWCI:
Cites:
Cited by:
Related to: