传统计算方法和机器学习在癌症药物重定位中的应用现状研究

doi:10.12114/j.issn.1007-9572.2024.0568

中国全科医学 ›› 2026, Vol. 29 ›› Issue (22): 3226-3232.DOI: 10.12114/j.issn.1007-9572.2024.0568

• 论著·用药指导 • 上一篇

传统计算方法和机器学习在癌症药物重定位中的应用现状研究

曹艺馨¹, 李永智², 魏灵茜¹, 周岩¹, 高菲¹, 于琦³^,⁴^,^*()

1．030001　山西省太原市，山西医科大学基础医学院
2．030001　山西省太原市，山西医科大学口腔医学院
3．030600　山西省晋中市，山西医科大学管理学院
4．030600　山西省晋中市，临床决策研究大数据山西省重点实验室

收稿日期:2025-08-10 修回日期:2025-12-10 出版日期:2026-08-05 发布日期:2026-07-08
通讯作者: 于琦
作者贡献：
曹艺馨负责文章的构思与设计、论文撰写；李永智负责论文修订、监督管理；魏灵茜、周岩、高菲负责研究资料的收集与整理；于琦负责文章的质量控制及审校、最终版本修订，对论文负责。
本文为中文翻译版本，原文"Application status of traditional computational methods and machine learning in cancer drug repositioning"，已获得授权。翻译与出版遵循COPE和ICMJE关于二次发表的指南。
基金资助:
山西省基础研究计划(202303021221132); 山西省科技创新人才团队专项资助(202304051001017); 山西省高等学校大学生创新创业训练计划项目(20240447)

Application Status of Traditional Computational Methods and Machine Learning in Cancer Drug Repositioning

CAO Yixin¹, LI Yongzhi², WEI Lingxi¹, ZHOU Yan¹, GAO Fei¹, YU Qi³^,⁴^,^*()

1. School of Basic Medicine, Shanxi Medical University, Taiyuan 030001, China
2. School of Stomatology, Shanxi Medical University, Taiyuan 030001, China
3. School of Management, Shanxi Medical University, Jinzhong 030600, China
4. Shanxi Key Laboratory of Big Data for Clinical Decision Research, Shanxi Medical University, Jinzhong 030600, China

Received:2025-08-10 Revised:2025-12-10 Published:2026-08-05 Online:2026-07-08
Contact: YU Qi

摘要/Abstract

摘要： 全球癌症负担的不断增加促使人们大力研究并试图开发更有效的抗肿瘤药物。然而，开发新药的成本昂贵。本文介绍了一种经济有效的策略——药物重定位，可以将已批准的药物重新用于新的适应证。本文对癌症药物重定位的计算策略进行了回顾，重点关注机器学习。近年来，生物信息学技术和多组学数据的融合极大促进了癌症药物的重新利用。机器学习和深度学习尤其促成了癌症药物重定位。本综述总结了传统计算方法和机器学习在该领域的应用现状，分析表明无论是独立的还是与其他基于生物信息学的方法相结合，机器学习在促进癌症药物重定位方面都具有很大前景。本文可以为癌症药物重定位的计算策略与癌症药物研发相结合的进一步发展提供参考。

关键词: 癌症, 药物重定位, 机器学习, 深度学习, 生物信息学

Abstract:

The escalating global cancer burden has spurred extensive research and development efforts towards effective anti-cancer agents. Nevertheless, the exorbitant cost of developing novel drugs poses a significant challenge. This paper describes a cost-effective strategy, drug repositioning, which reuses approved drugs for novel medical indications, potentially resolving this predicament. This paper presents a comprehensive review of computational strategies for cancer drug repositioning, with an emphasis on machine learning. In recent years, the integration of bioinformatics technology and multi-omics data has significantly propelled the progress of cancer drug repurposing. In particular, machine learning and deep learning have contributed significantly to the remarkable advancements in cancer drug repositioning. This review summarizes the current application status of traditional computational methods and machine learning in this domain, and the analysis shows that machine learning holds great promise in facilitating cancer drug repositioning, both independently and in combination with other bioinformatics-based approaches. This paper can provide a valuable reference for the further integration of computational strategies and cancer drug research and development.

Key words: Cancer, Drug repositioning, Machine learning, Deep learning, Bioinformatics

中图分类号:

R-05

曹艺馨,李永智,魏灵茜,等. 传统计算方法和机器学习在癌症药物重定位中的应用现状研究[J]. 中国全科医学, 2026, 29(22): 3226-3232. DOI: 10.12114/j.issn.1007-9572.2024.0568.
CAO Yixin,LI Yongzhi,WEI Lingxi, et al. Application Status of Traditional Computational Methods and Machine Learning in Cancer Drug Repositioning[J]. Chinese General Practice, 2026, 29(22): 3226-3232. DOI: 10.12114/j.issn.1007-9572.2024.0568.

图/表 2

参考文献 60

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步骤	检索式
#1	"drug"[All Fields]
#2	"repurposable"[All Fields]O R "repurpose"[All Fields]O R "repurposed"[All Fields]O R "repurposing"[All Fields]O R "reposit"[All Fields]O R "reposited"[All Fields]O R "reposition"[All Fields]O R "repositioned"[All Fields]O R "repositioning"[All Fields]O R "repositionings"[All Fields]O R "repositions"[All Fields]
#3	"cancers"[All Fields]O R "cancerated"[All Fields]O R "canceration"[All Fields]O R "cancerization"[All Fields]O R "cancerized"[All Fields]O R "cancerous"[All Fields]O R "neoplasms"[MeSH Terms]O R "neoplasms"[All Fields]O R "cancer"[All Fields]O R "cancers"[All Fields]O R "neurofibroma"[MeSH Terms]O R "neurofibroma"[All Fields]O R "neurofibromas"[All Fields]O R "tumor s"[All Fields]O R "tumoral"[All Fields]O R "tumorous"[All Fields]O R "tumour"[All Fields]O R "neoplasms"[MeSH Terms]O R "neoplasms"[All Fields]O R "tumor"[All Fields]O R "tumour s"[All Fields]O R "tumoural"[All Fields]O R "tumourous"[All Fields]O R "tumours"[All Fields]O R "tumors"[All Fields]O R "neoplasm s"[All Fields]O R "neoplasms"[MeSH Terms]O R "neoplasms"[All Fields]O R "neoplasm"[All Fields]
#4	"artificial intelligence"[All Fields]O R "machine learning"[All Fields]O R "deep learning"[All Fields]O R "neural network"[All Fields]O R "random forest"[All Fields]O R "support vector machine"[All Fields]O R "bayesian network"[All Fields]O R "algorithm s"[All Fields]O R "algorithmic"[All Fields]O R "algorithmically"[All Fields]O R "algorithmics"[All Fields]O R "algorithmization"[All Fields]O R "algorithms"[MeSH Terms]O R "algorithms"[All Fields]O R "algorithm"[All Fields]O R "data mining"[All Fields]O R "text mining"[All Fields]O R "natural language processing"[All Fields]O R "non-natural language processing"[All Fields]
#5	（#3）AND（#4）

步骤	检索式
#1	"drug"[All Fields]
#2	"repurposable"[All Fields]O R "repurpose"[All Fields]O R "repurposed"[All Fields]O R "repurposing"[All Fields]O R "reposit"[All Fields]O R "reposited"[All Fields]O R "reposition"[All Fields]O R "repositioned"[All Fields]O R "repositioning"[All Fields]O R "repositionings"[All Fields]O R "repositions"[All Fields]
#3	"cancers"[All Fields]O R "cancerated"[All Fields]O R "canceration"[All Fields]O R "cancerization"[All Fields]O R "cancerized"[All Fields]O R "cancerous"[All Fields]O R "neoplasms"[MeSH Terms]O R "neoplasms"[All Fields]O R "cancer"[All Fields]O R "cancers"[All Fields]O R "neurofibroma"[MeSH Terms]O R "neurofibroma"[All Fields]O R "neurofibromas"[All Fields]O R "tumor s"[All Fields]O R "tumoral"[All Fields]O R "tumorous"[All Fields]O R "tumour"[All Fields]O R "neoplasms"[MeSH Terms]O R "neoplasms"[All Fields]O R "tumor"[All Fields]O R "tumour s"[All Fields]O R "tumoural"[All Fields]O R "tumourous"[All Fields]O R "tumours"[All Fields]O R "tumors"[All Fields]O R "neoplasm s"[All Fields]O R "neoplasms"[MeSH Terms]O R "neoplasms"[All Fields]O R "neoplasm"[All Fields]
#4	"artificial intelligence"[All Fields]O R "machine learning"[All Fields]O R "deep learning"[All Fields]O R "neural network"[All Fields]O R "random forest"[All Fields]O R "support vector machine"[All Fields]O R "bayesian network"[All Fields]O R "algorithm s"[All Fields]O R "algorithmic"[All Fields]O R "algorithmically"[All Fields]O R "algorithmics"[All Fields]O R "algorithmization"[All Fields]O R "algorithms"[MeSH Terms]O R "algorithms"[All Fields]O R "algorithm"[All Fields]O R "data mining"[All Fields]O R "text mining"[All Fields]O R "natural language processing"[All Fields]O R "non-natural language processing"[All Fields]
#5	（#3）AND（#4）

传统计算方法和机器学习在癌症药物重定位中的应用现状研究

Application Status of Traditional Computational Methods and Machine Learning in Cancer Drug Repositioning

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