Integrating sequence and graph information for enhanced drug-target affinity prediction

作     者：Haohuai HE Guanxing CHEN Calvin Yu-Chian CHEN 

作者机构：Artificial Intelligence Medical Research CenterSchool of Intelligent Systems EngineeringShenzhen Campus of Sun Yat-sen University 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2024年第67卷第2期

页      面：325-326页

核心收录：

学科分类：1007[医学-药学(可授医学、理学学位)] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 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）] 10[医学] 

基　　金：supported by National Natural Science Foundation of China (Grant No.62176272) Research and Development Program of Guangzhou Science and Technology Bureau (Grant No.2023B01J1016) Key-Area Research and Development Program of Guangdong Province (Grant No.2020B1111100001) 

摘      要：Drug discovery is a pivotal discipline involving the identification and development of innovative pharmaceuticals designed to combat a wide array of illnesses. It plays a crucial role in advancing global health outcomes and improving the standard of living for individuals worldwide. However,drug discovery often relies on laborious in vitro experiments,