artificialintelligenceindrugdesign
文献类型：期刊论文
| 作者 | Zhong Feisheng1; Xing Jing1; Li Xutong1; Liu Xiaohong1; Fu Zunyun1; Xiong Zhaoping1; Lu Dong1; Wu Xiaolong1; Zhao Jihui1; Tan Xiaoqin1 |
| 刊名 | sciencechinalifesciences
 |
| 出版日期 | 2018 |
| 卷号 | 61期号:10页码:1191 |
| 关键词 | PROTEIN-PROTEIN-INTERACTION MACHINE LEARNING-METHODS COMPUTATIONAL METHODS MOLECULAR-PROPERTIES INTERACTION NETWORKS NEURAL-NETWORKS PREDICTION DOCKING DISCOVERY PERMEABILITY drug design artificial intelligence deep learning QSAR ADME/T |
| ISSN号 | 1674-7305 |
| DOI | 10.1007/s11427-018-9342-2 |
| 英文摘要 | Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology, the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials. Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence (AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity (ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability, deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules, which will further promote the application of AI technologies in the field of drug design. |
| 资助项目 | [National Natural Science Foundation of China] ; ["Personalized Medicines-Molecular Signature-based Drug Discovery and Development", Strategic Priority Research Program of the Chinese Academy of Sciences] ; [National Key Research & Development Plan] ; [National Basic Research Program] |
| 语种 | 英语 |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/287814]  |
| 专题 | 中国科学院上海药物研究所 |
| 作者单位 | 1.中国科学院上海药物研究所 2.德州学院 |
| 推荐引用方式 GB/T 7714 | Zhong Feisheng,Xing Jing,Li Xutong,et al. artificialintelligenceindrugdesign[J]. sciencechinalifesciences,2018,61(10):1191. |
| APA | Zhong Feisheng.,Xing Jing.,Li Xutong.,Liu Xiaohong.,Fu Zunyun.,...&Jiang Hualiang.(2018).artificialintelligenceindrugdesign.sciencechinalifesciences,61(10),1191. |
| MLA | Zhong Feisheng,et al."artificialintelligenceindrugdesign".sciencechinalifesciences 61.10(2018):1191. |
入库方式： OAI收割
来源：上海药物研究所
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