Review on QSAR using Anticancer Drug

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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