Computing the relative binding affinity of ligands based on a pairwise binding comparison network
文献类型：期刊论文
| 作者 | Yu, Jie1,2,3; Li, Zhaojun4,5; Chen, Geng1,6,7; Kong, Xiangtai1,6; Hu, Jie8; Wang, Dingyan1,3; Cao, Duanhua1,9; Li, Yanbei1,6,7; Huo, Ruifeng8; Wang, Gang1,6 |
| 刊名 | Nature computational science
 |
| 出版日期 | 2023-10-19 |
| 卷号 | 3期号:10页码:860-872 |
| ISSN号 | 2662-8457 |
| DOI | 10.1038/s43588-023-00529-9 |
| 文献子类 | Article |
| 英文摘要 | Structure-based lead optimization is an open challenge in drug discovery, which is still largely driven by hypotheses and depends on the experience of medicinal chemists. Here we propose a pairwise binding comparison network (PBCNet) based on a physics-informed graph attention mechanism, specifically tailored for ranking the relative binding affinity among congeneric ligands. Benchmarking on two held-out sets (provided by Schrodinger and Merck) containing over 460 ligands and 16 targets, PBCNet demonstrated substantial advantages in terms of both prediction accuracy and computational efficiency. Equipped with a fine-tuning operation, the performance of PBCNet reaches that of Schrodinger's FEP+, which is much more computationally intensive and requires substantial expert intervention. A further simulation-based experiment showed that active learning-optimized PBCNet may accelerate lead optimization campaigns by 473%. Finally, for the convenience of users, a web service for PBCNet is established to facilitate complex relative binding affinity prediction through an easy-to-operate graphical interface. |
| 语种 | 英语 |
| WOS记录号 | MEDLINE: 38177766 |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/309615]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Li, Xutong; Luo, Xiaomin; Zheng, Mingyue |
| 作者单位 | 1.Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China. 2.School of Information Science and Technology, Shanghai Tech University, Shanghai, China. 3.Lingang Laboratory, Shanghai, China. 4.College of Computer and Information Engineering, Dezhou University, Dezhou City, China. 5.Development Department, Suzhou Alphama Biotechnology Co., Ltd, Suzhou City, China. 6.University of Chinese Academy of Sciences, Beijing, China. 7.School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China. 8.School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China. 9.Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China. 10.Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China. lixutong@simm.ac.cn. |
| 推荐引用方式 GB/T 7714 | Yu, Jie,Li, Zhaojun,Chen, Geng,et al. Computing the relative binding affinity of ligands based on a pairwise binding comparison network[J]. Nature computational science,2023,3(10):860-872. |
| APA | Yu, Jie.,Li, Zhaojun.,Chen, Geng.,Kong, Xiangtai.,Hu, Jie.,...&Zheng, Mingyue.(2023).Computing the relative binding affinity of ligands based on a pairwise binding comparison network.Nature computational science,3(10),860-872. |
| MLA | Yu, Jie,et al."Computing the relative binding affinity of ligands based on a pairwise binding comparison network".Nature computational science 3.10(2023):860-872. |
入库方式： OAI收割
来源：上海药物研究所
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