• Feisheng Zhong, Jing Xing, Xutong Li, Xiaohong Liu, Zunyun Fu, Zhaoping Xiong, Dong Lu, Xiaolong Wu, Jihui Zhao, Xiaoqin Tan, Fei Li, Xiaomin Luo, Zhaojun Li, Kaixian Chen, Mingyue Zheng, and Hualiang Jiang.
• Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
• Sci China Life Sci. 2018 Oct 1; 61 (10): 1191-1204.

AbstractThanks 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.

16 keywords...

hide…