A review of deep learningg methods for ligand based drug virtual screening

作     者：Hongjie Wu Junkai Liu Runhua Zhang Yaoyao Lu Guozeng Cui Zhiming Cui Yijie Ding 

作者机构：School of Electronic and Information EngineeringSuzhou University of Science and TechnologySuzhou 215009China Yangtze Delta Region Institute(Quzhou)University of Electronic Science and Technology of ChinaQuzhou 32400China 

出 版 物：《Fundamental Research》 (自然科学基础研究（英文版）)

年 卷 期：2024年第4卷第4期

页      面：715-737页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the National Natural Science Foundation of China(62073231,62176175,62172076) National Research Project(2020YFC2006602) Provincial Key Laboratory for Computer Information Processing Technology,Soochow University(KJS2166) Opening Topic Fund of Big Data Intelligent Engineering Laboratory of Jiangsu Province(SDGC2157) Postgraduate Research&Practice Innovation Program of Jiangsu Province 

主　　题：Virtual screening Deeplearning Drug discovery Drug-targetinteraction Drug-target affinity 

摘      要：Drug discovery is costly and time consuming,and modern drug discovery endeavors are progressively reliant on computational methodologies,aiming to mitigate temporal and financial expenditures associated with the *** particular,the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 ***,the performance of deep learning methods in drug virtual screening has been particularly *** has become a concern for researchers how to summarize the existing deep learning in drug virtual screening,select different models for different drug screening problems,exploit the advantages of deep learning models,and further improve the capability of deep learning in drug virtual *** review first introduces the basic concepts of drug virtual screening,common datasets,and data representation ***,large numbers of common deep learning methods for drug virtual screening are compared and *** addition,a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual ***,the existing challenges and future directions in the field of virtual screening are presented.