Dynamic Visualization of Computer-Aided Peptide Design for Cancer Therapeutics.

IF 5.1 2区医学 Q1 CHEMISTRY, MEDICINAL Drug Design, Development and Therapy Pub Date : 2025-02-15 eCollection Date: 2025-01-01 DOI:10.2147/DDDT.S497126

Dan Hou, Haobin Zhou, Yuting Tang, Ziyuan Liu, Lin Su, Junkai Guo, Janak Lal Pathak, Lihong Wu

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Abstract

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.

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用于癌症治疗的计算机辅助肽设计的动态可视化。

目的：癌症是一个重大的全球公共卫生问题，传统疗法可能产生严重的副作用。以肽为基础的肿瘤治疗因其靶向性好、生物相容性好、合成方便等优点，越来越多地应用于癌症等疾病。随着计算机技术和生物信息学的发展，基于计算机技术的合理设计策略已被用于开发更具成本效益和有效的抗癌肽。本研究旨在通过文献计量学分析，探讨计算机辅助设计癌症治疗多肽领域的现状、热点和未来趋势。方法：收集Web of Science Core Collection中2006 ~ 2024年发表的相关文献1547篇。使用CiteSpace、VOSviewer、Bibliometrix、Origin等工具和在线文献计量平台进行文献计量分析。结果：该领域的研究呈现出稳步增长的趋势，其中美国和中国的贡献最为显著。目前，ACP研究主要集中在与药物传递相关的细胞穿透肽，有望成为未来的研究热点。除此之外，与免疫疗法相关的肽疫苗也值得关注。此外，分子动力学模拟和分子对接是目前比较流行的研究方法。与此同时，深度学习是新兴的关键词，这表明它对未来肽设计的影响可能更大。结论：深度学习技术是新兴的研究热点，具有巨大的潜力和广阔的前景。细胞穿透肽和多肽免疫治疗作为前沿研究方向，有望在癌症治疗中取得突破。本研究为肿瘤治疗中多肽的计算机辅助设计提供了有价值的见解，对推进该领域的深入研究和应用具有重要意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Drug Design, Development and Therapy CHEMISTRY, MEDICINAL-PHARMACOLOGY & PHARMACY

CiteScore

9.00

自引率

0.00%

发文量

382

审稿时长

>12 weeks

期刊介绍： Drug Design, Development and Therapy is an international, peer-reviewed, open access journal that spans the spectrum of drug design, discovery and development through to clinical applications. The journal is characterized by the rapid reporting of high-quality original research, reviews, expert opinions, commentary and clinical studies in all therapeutic areas. Specific topics covered by the journal include: Drug target identification and validation Phenotypic screening and target deconvolution Biochemical analyses of drug targets and their pathways New methods or relevant applications in molecular/drug design and computer-aided drug discovery* Design, synthesis, and biological evaluation of novel biologically active compounds (including diagnostics or chemical probes) Structural or molecular biological studies elucidating molecular recognition processes Fragment-based drug discovery Pharmaceutical/red biotechnology Isolation, structural characterization, (bio)synthesis, bioengineering and pharmacological evaluation of natural products** Distribution, pharmacokinetics and metabolic transformations of drugs or biologically active compounds in drug development Drug delivery and formulation (design and characterization of dosage forms, release mechanisms and in vivo testing) Preclinical development studies Translational animal models Mechanisms of action and signalling pathways Toxicology Gene therapy, cell therapy and immunotherapy Personalized medicine and pharmacogenomics Clinical drug evaluation Patient safety and sustained use of medicines.