抗癌药物研发过程中的传统与新型计算机辅助药物设计（CADD）方法。
Traditional and Novel Computer-Aided Drug Design (CADD) Approaches in the Anticancer Drug Discovery Process.
作者信息
Del Carmen Quintal Bojórquez Nidia, Campos Maira Rubi Segura
机构信息
Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Periférico Norte Km. 33.5, Tablaje Catastral 13615, Colonia Chuburna de Hidalgo Inn. Merida, Yucatan, Mexico. C.P. 97203, Mexico.
出版信息
Curr Cancer Drug Targets. 2023;23(5):333-345. doi: 10.2174/1568009622666220705104249.
BACKGROUND
In the last decade, cancer has been a leading cause of death worldwide. Despite the impressive progress in cancer therapy, firsthand treatments are not selective to cancer cells and cause serious toxicity. Thus, the design and development of selective and innovative small molecule drugs is of great interest, particularly through in silico tools.
OBJECTIVE
The aim of this review is to analyze different subsections of computer-aided drug design (CADD) in the process of discovering anticancer drugs.
METHODS
Articles from the 2008-2021 timeframe were analyzed and based on the relevance of the information and the JCR of its journal of precedence, were selected to be included in this review.
RESULTS
The information collected in this study highlights the main traditional and novel CADD approaches used in anticancer drug discovery, its sub-segments, and some applied examples. Throughout this review, the potential use of CADD in drug research and discovery, particularly in the field of oncology, is evident due to the many advantages it presents.
CONCLUSION
CADD approaches play a significant role in the drug development process since they allow a better administration of resources with successful results and a promising future market and clinical wise.
背景
在过去十年中，癌症一直是全球主要的死因。尽管癌症治疗取得了令人瞩目的进展，但一线治疗方法对癌细胞没有选择性，且会导致严重的毒性。因此，设计和开发选择性的创新小分子药物备受关注，尤其是通过计算机模拟工具。
目的
本综述的目的是分析计算机辅助药物设计（CADD）在抗癌药物发现过程中的不同子领域。
方法
分析了2008 - 2021年期间的文章，并根据信息的相关性及其优先发表期刊的期刊引用报告（JCR），选择纳入本综述。
结果
本研究收集的信息突出了抗癌药物发现中使用的主要传统和新型CADD方法、其子领域以及一些应用实例。在本综述中，由于CADD具有诸多优势，其在药物研究和发现中的潜在应用，尤其是在肿瘤学领域，显而易见。
结论
CADD方法在药物开发过程中发挥着重要作用，因为它们能够更好地管理资源，取得成功的结果，并拥有前景广阔的未来市场和临床应用前景。
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