化学信息学
大数据
药物发现
计算机科学
数据科学
化学空间
知识抽取
虚拟筛选
人工智能
数据挖掘
生物信息学
生物
作者
Manish Tripathi,Abhigyan Nath,T.P. Singh,A.S. Ethayathulla,Punit Kaur
出处
期刊:Molecular Diversity
[Springer Science+Business Media]
日期:2021-06-23
卷期号:25 (3): 1439-1460
被引量:135
标识
DOI:10.1007/s11030-021-10256-w
摘要
The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug discovery processes such as virtual drug screening, de novo molecule design and discovery in this big data era. The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space. The present review summarizes the role of big data and AI techniques currently being implemented to satisfy the ever-increasing research demands in drug discovery pipelines.
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