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Tora3D: an autoregressive torsion angle prediction model for molecular 3D conformation generation

文献类型：期刊论文

作者Zhang, Zimei2,3; Wang, Gang3,4; Li, Rui3,6; Ni, Lin3,5; Zhang, RunZe3,4; Cheng, Kaiyang3,5; Ren, Qun3,5; Kong, Xiangtai3,4; Ni, Shengkun3,4; Tong, Xiaochu3,4
刊名JOURNAL OF CHEMINFORMATICS
出版日期2023-06-07
卷号15期号:1页码:14
ISSN号1758-2946
关键词Conformations generation Autoregressive Transformer Deep learning Small molecules
DOI10.1186/s13321-023-00726-8
通讯作者Zheng, Mingyue(myzheng@simm.ac.cn) ; Li, Xutong(lixutong@simm.ac.cn)
英文摘要Three-dimensional (3D) conformations of a small molecule profoundly affect its binding to the target of interest, the resulting biological effects, and its disposition in living organisms, but it is challenging to accurately characterize the conformational ensemble experimentally. Here, we proposed an autoregressive torsion angle prediction model Tora3D for molecular 3D conformer generation. Rather than directly predicting the conformations in an end-to-end way, Tora3D predicts a set of torsion angles of rotatable bonds by an interpretable autoregressive method and reconstructs the 3D conformations from them, which keeps structural validity during reconstruction. Another advancement of our method over other conformational generation methods is the ability to use energy to guide the conformation generation. In addition, we propose a new message-passing mechanism that applies the Transformer to the graph to solve the difficulty of remote message passing. Tora3D shows superior performance to prior computational models in the trade-off between accuracy and efficiency, and ensures conformational validity, accuracy, and diversity in an interpretable way. Overall, Tora3D can be used for the quick generation of diverse molecular conformations and 3D-based molecular representation, contributing to a wide range of downstream drug design tasks.
WOS关键词FORCE-FIELD
资助项目National Natural Science Foundation of China[T2225002] ; National Natural Science Foundation of China[82273855] ; National Natural Science Foundation of China[82204278] ; National Natural Science Foundation of China[91953203] ; Lingang Laboratory[LG202102-01-02] ; National Key Research and Development Program of China[2022YFC3400504] ; China Postdoctoral Science Foundation[2022M720153] ; Shanghai Sailing Program[22YF1460800]
WOS研究方向Chemistry ; Computer Science
语种英语
出版者BMC
WOS记录号WOS:001004220300001
源URL[http://119.78.100.183/handle/2S10ELR8/306266]  
专题新药研究国家重点实验室
通讯作者Zheng, Mingyue; Li, Xutong
作者单位1.Fourth Mil Med Univ, Tangdu Hosp, Precis Pharm & Drug Dev Ctr, Dept Pharm, Xian 710038, Peoples R China
2.Univ Sci & Technol China, Div Life Sci & Med, Hefei 230026, Anhui, Peoples R China
3.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China
4.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
5.Nanjing Univ Chinese Med, 138 Xianlin Rd, Nanjing 210023, Peoples R China
6.China Pharmaceut Univ, Sch Pharm, 639 Longmian Rd, Nanjing 211198, Peoples R China
7.Lingang Lab, Shanghai 200031, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Zimei,Wang, Gang,Li, Rui,et al. Tora3D: an autoregressive torsion angle prediction model for molecular 3D conformation generation[J]. JOURNAL OF CHEMINFORMATICS,2023,15(1):14.
APA Zhang, Zimei.,Wang, Gang.,Li, Rui.,Ni, Lin.,Zhang, RunZe.,...&Li, Xutong.(2023).Tora3D: an autoregressive torsion angle prediction model for molecular 3D conformation generation.JOURNAL OF CHEMINFORMATICS,15(1),14.
MLA Zhang, Zimei,et al."Tora3D: an autoregressive torsion angle prediction model for molecular 3D conformation generation".JOURNAL OF CHEMINFORMATICS 15.1(2023):14.

入库方式： OAI收割

来源：上海药物研究所

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