Dynamic Visualization of Computer-Aided Peptide Design for Cancer Therapeutics

被引：0

作者：

Hou, Dan ^{[1
,2
,3
,4
]}

Zhou, Haobin ^{[1
,2
]}

Tang, Yuting ^{[1
,2
]}

Liu, Ziyuan ^{[1
,2
]}

Su, Lin ^{[1
,2
]}

Guo, Junkai ^{[1
,2
]}

Pathak, Janak Lal ^{[1
,2
]}

Wu, Lihong ^{[1
,2
]}

机构：

[1] Guangzhou Med Univ, Sch & Hosp Stomatol, Guangdong Engn Res Ctr Oral Restorat & Reconstruct, Dept Basic Oral Med, Guangzhou 510182, Guangdong, Peoples R China

[2] Guangzhou Key Lab Basic & Appl Res Oral Regenerat, Guangzhou 510182, Guangdong, Peoples R China

[3] Vrije Univ Amsterdam, Amsterdam UMC VUmc, Dept Oral & Maxillofacial Surg Oral Pathol, NL-1081HZ Amsterdam, Netherlands

[4] Vrije Univ Amsterdam, Acad Ctr Dent Amsterdam ACTA, Amsterdam Movement Sci, NL-1081HZ Amsterdam, Netherlands

来源：

DRUG DESIGN DEVELOPMENT AND THERAPY | 2025年 / 19卷

关键词：

peptide design; CiteSpace; VOSviewer; visualization; research trend; GENETIC ALGORITHM; ANTICANCER PEPTIDES; DOCKING; PREDICTION; APOPTOSIS; IACP; TOOL;

D O I：

10.2147/DDDT.S497126

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

Purpose: Cancer stands as a significant global public health concern, with traditional therapies potentially yielding severe side effects. Peptide-based cancer therapy is increasingly employed for diseases like cancer due to its advantages of excellent targeting, biocompatibility, and convenient synthesis. With advancements in computer technology and bioinformatics, rational design strategies based on computer technology have been employed to develop more cost-effective and potent anticancer peptides (ACPs). This study aims to explore the current status, hotspots, and future trends in the field of computer-aided design of peptides for cancer treatment through a bibliometric analysis. Methods: A total of 1547 relevant publications published from 2006 to 2024 were collected from the Web of Science Core Collection. Bibliometric analysis was conducted using tools like CiteSpace, VOSviewer, Bibliometrix, Origin, and an online bibliometric platform. Results: The research in this field has shown a steady growth trend, with the United States and China making the most significant contributions. Currently, ACP research mainly focuses on cell-penetrating peptides related to drug delivery, which are expected to become future research hotspots. Beyond that, peptide vaccines associated with immunotherapy are also worthy of attention. In addition, molecular dynamics simulation and molecular docking are currently popular research methods. At the same time, deep learning is the emerging keyword, indicating its potential for a more significant impact on future peptide design. Conclusion: Deep learning technology represents emerging research hotspots with immense potential and promising prospects. As cutting-edge research directions, cellularly penetrating peptides and polypeptide immunotherapy are expected to achieve breakthroughs in cancer treatment. This study provides valuable insights into the computer-aided design of peptides in cancer therapy, contributing significantly to advancing the in-depth research and applications in this area.

引用

页码：1043 / 1065

页数：23