药物发现
计算机科学
计算机辅助
过程(计算)
药品
计算机辅助设计
生化工程
数据科学
人工智能
工程类
医学
生物信息学
药理学
生物
操作系统
程序设计语言
作者
Divya Vemula,Perka Jayasurya,Varthiya Sushmitha,Yethirajula Naveen Kumar,Vasundhra Bhandari
标识
DOI:10.1016/j.ejps.2022.106324
摘要
Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.
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