A Review of Deep Learning Application on Drug Activity Prediction

doi:10.16476/j.pibb.2021.0161

	A Review of Deep Learning Application on Drug Activity Prediction
	Liu Li-Mei 1; Chen Xiao-Jin 1; Sun Shi-Wei 2; Wang Yu 2; Wang Hui 2; Mei Shu-Li 1; Wang Yao-Jun 1
	2022-08-01
发表期刊	PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS
ISSN	1000-3282
卷号	49 期号:8 页码:1498-1519
摘要	It takes a long time for a drug to go from research and development to clinical application, and the investment cost during the period can reach one billion yuan. The combination of medicine and artificial and the development of big data of biochemistry contribute to sharply increasing drug activity data, and traditional experimental methods for drug activity prediction and discovery are hard to meet the needs of drug research and development. Algorithms are used to assist drug development and solve various problems during the process to significantly accelerate drug development. Traditional machine learning methods, especially random forests, support vector machines, and artificial neural networks, can improve drug activity prediction accuracy. Due to the multi-layer neural networks of deep learning, the model can process high-dimensional input variables and there is no need to limit the amount of input data characteristics manually. Deep learning can build a more complex function, and its application in drug research and development can further improve the efficiency of each step of drug research. Widely used deep learning models in drug activity are mainly DNN (deep neural networks), RNN (recurrent neural networks), and AE (auto encoder). GAN (generative adversarial networks) is often used in combination with other models for data enhancement due to its ability to generate data. Researches and applications of deep learning in drug molecule activity prediction in recent years showed that the accuracy and efficiency of deep learning models were higher than traditional experimental methods and traditional machine learning methods. Therefore, deep learning is expected to become the most critical auxiliary calculation model in drug research and development in the next decade.
关键词	machine learning deep learning molecular drug activity prediction
DOI	10.16476/j.pibb.2021.0161
收录类别	SCI
语种	英语
资助项目	Beijing Nunicipal Natural Science Foundation[5214026]
WOS研究方向	Biochemistry & Molecular Biology ; Biophysics
WOS类目	Biochemistry & Molecular Biology ; Biophysics
WOS记录号	WOS:000905557100011
出版者	CHINESE ACAD SCIENCES, INST BIOPHYSICS
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/20107
专题	中国科学院计算技术研究所期刊论文
通讯作者	Mei Shu-Li; Wang Yao-Jun
作者单位	1.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu Li-Mei,Chen Xiao-Jin,Sun Shi-Wei,et al. A Review of Deep Learning Application on Drug Activity Prediction[J]. PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS,2022,49(8):1498-1519.
APA	Liu Li-Mei.,Chen Xiao-Jin.,Sun Shi-Wei.,Wang Yu.,Wang Hui.,...&Wang Yao-Jun.(2022).A Review of Deep Learning Application on Drug Activity Prediction.PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS,49(8),1498-1519.
MLA	Liu Li-Mei,et al."A Review of Deep Learning Application on Drug Activity Prediction".PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS 49.8(2022):1498-1519.