创新链/学科链/研发链/产业链

新药研发前沿动态 / 医药领域趋势进展

基于机器学习和大数据挖掘的药物重定位算法综述

A Review on In-Silico Repositioning Algorithms of Drugs and Chemical Compounds

  • 摘要: 药物重定位(又称药物重使用或药物重配置)是将现有治疗方法应用于新的疾病的过程的一种药物研发方法。新药研发成本高、失败率高,使得对现有药物进行重新定位成为目前研究的热点。在高通量测序技术的帮助下,许多有效的算法被提出并应用于药物的重新定位。目前用于药物和化合物重定位的算法可分为基于特征的方法、基于矩阵分解的方法、基于网络的方法3大类。分别对这3类常用方法进行综述,总结这些方法的优缺点,并对未来药物重新定位方法的发展方向进行剖析,以期达到帮助我国科研工作者开发更加有效的药物重定位算法,增加我国社会经济效益的目的。

     

    Abstract: Drug repositioning (also known as drug repurposing or drug reprofiling) is the process of applying existing therapeutics to new disease indications. Due to the high cost and high failure rate of research and development of new drugs, the repositioning of existing drugs has become a hot topic of current research. With the help of high-throughput sequencing technology, many effective algorithms have been proposed and applied to drug repositioning. Current algorithms for the repositioning of drugs and chemical compounds can be divided into three categories:feature-based, matrix-based and network-based relocation or decomposition. With a view to helping Chinese researchers to develop more effective drug relocation algorithms and increasing the social and economic benefits of our country, this paper reviews the three commonly used methods, summarizes their advantages and disadvantages, and analyzes the future development direction of drug repositioning.

     

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