UniBind:a novel artificial intelligence-based prediction model for SARS-CoV-2 infectivity and variant evolution  

作　　者：Qihong Yan Jincun Zhao 

机构地区：[1]State Key Laboratory of Respiratory Disease,National Clinical Research Center for Respiratory Disease,Guangzhou Institute of Respiratory Health,the First Affiliated Hospital of Guangzhou Medical University,Guangzhou,China

出　　处：《Signal Transduction and Targeted Therapy》2024年第1期14-15,共2页信号转导与靶向治疗（英文）

基　　金：the National Natural Science Foundation of China:82201932,82025001;ZhongNanShan Medical Foundation of Guangdong Province:ZNSA-2020012;Guangdong Basic and Applied Basic Research Foundation:2022B1515020059,2021B1515130005.

摘　　要：In a recent study published in Nature Medicine,Wang et al.developed an excellent framework called UniBind based on artificial intelligence(AI),which enables accurately predicting infectivity of SARS-CoV-2 variants and evolutionary trends of future viral variants.1 This computational method holds the possibility to not only serve as a valuable early-warning tool for monitoring potential pathogenic SARS-CoV-2 variants but also facilitate fundamental research on protein-protein interactions(PPIs).

关 键 词：artificial BIND PREDICTION 

分 类 号：R373[医药卫生—病原生物学]