药物发现
抗癌药
药品
计算机科学
药物开发
管道(软件)
过程(计算)
制药工业
数据科学
风险分析(工程)
医学
药理学
生物信息学
生物
操作系统
程序设计语言
作者
Hemant Ahirwar,Gabbar Kurmi,Rubeena Khan,Basant Khare,Anushree Jain,Prateek Jain,Bhupendra Singh Thakur
标识
DOI:10.22270/ajdhs.v2i4.27
摘要
New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are demanded for a new drug discovery via traditional drug development pipeline. How to reduce the research cost and speed up the development process of new drug discovery has become a challenging, urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology for faster, cheaper and more effective drug design. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. In this work, we discussed the Qualitative structure activity relationship, a computer-aided drug discovery process with a focus on anticancer drugs. Keywords: New drug discovery, Computer-aided drug discovery, the Qualitative structure activity relationship, Anticancer
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