中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
A Review of Deep Learning Application on Drug Activity Prediction

文献类型：期刊论文

作者Liu Li-Mei2; Chen Xiao-Jin2; Sun Shi-Wei1; Wang Yu1; Wang Hui1; Mei Shu-Li2; Wang Yao-Jun2
刊名PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS
出版日期2022-08-01
卷号49期号:8页码:1498-1519
关键词machine learning deep learning molecular drug activity prediction
ISSN号1000-3282
DOI10.16476/j.pibb.2021.0161
英文摘要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.
资助项目Beijing Nunicipal Natural Science Foundation[5214026]
WOS研究方向Biochemistry & Molecular Biology ; Biophysics
语种英语
WOS记录号WOS:000905557100011
出版者CHINESE ACAD SCIENCES, INST BIOPHYSICS
源URL[http://119.78.100.204/handle/2XEOYT63/20106]  
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.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
2.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, 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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。