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Gram matrix: an efficient representation of molecular conformation and learning objective for molecular pretraining

文献类型：期刊论文

作者Xiang, Wenkai5; Zhong, Feisheng2,3,4; Ni, Lin1,4; Zheng, Mingyue1,3,4; Li, Xutong3,4; Shi, Qian5; Wang, Dingyan5
刊名BRIEFINGS IN BIOINFORMATICS
出版日期2024-07-11
卷号25期号:4页码:13
关键词gram matrix graph transformer molecular property prediction
ISSN号1467-5463
DOI10.1093/bib/bbae340
通讯作者Li, Xutong(lixutong@simm.ac.cn) ; Shi, Qian(shiqian@lglab.ac.cn) ; Wang, Dingyan(wangdy@lglab.ac.cn)
英文摘要Accurate prediction of molecular properties is fundamental in drug discovery and development, providing crucial guidance for effective drug design. A critical factor in achieving accurate molecular property prediction lies in the appropriate representation of molecular structures. Presently, prevalent deep learning-based molecular representations rely on 2D structure information as the primary molecular representation, often overlooking essential three-dimensional (3D) conformational information due to the inherent limitations of 2D structures in conveying atomic spatial relationships. In this study, we propose employing the Gram matrix as a condensed representation of 3D molecular structures and for efficient pretraining objectives. Subsequently, we leverage this matrix to construct a novel molecular representation model, Pre-GTM, which inherently encapsulates 3D information. The model accurately predicts the 3D structure of a molecule by estimating the Gram matrix. Our findings demonstrate that Pre-GTM model outperforms the baseline Graphormer model and other pretrained models in the QM9 and MoleculeNet quantitative property prediction task. The integration of the Gram matrix as a condensed representation of 3D molecular structure, incorporated into the Pre-GTM model, opens up promising avenues for its potential application across various domains of molecular research, including drug design, materials science, and chemical engineering.
WOS关键词DESCRIPTORS ; GENERATION
资助项目Shanghai Rising-Star Program[23QD1400600] ; Shanghai Sailing Program[22YF1460800] ; National Natural Science Foundation of China[82204278] ; National Natural Science Foundation of China[T2225002] ; National Key Research and Development Program of China[2022YFC3400504]
WOS研究方向Biochemistry & Molecular Biology ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:001283189900001
出版者OXFORD UNIV PRESS
源URL[http://119.78.100.183/handle/2S10ELR8/312586]  
专题新药研究国家重点实验室
通讯作者Li, Xutong; Shi, Qian; Wang, Dingyan
作者单位1.Nanjing Univ Chinese Med, 138 Xianlin Rd, Nanjing 210023, Peoples R China
2.Fujian Med Univ, Sch Pharm, Fujian Key Lab Drug Target Discovery & Struct & Fu, Fuzhou 350122, Peoples R China
3.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China
5.Lingang Lab, Shanghai 200031, Peoples R China
推荐引用方式
GB/T 7714		 Xiang, Wenkai,Zhong, Feisheng,Ni, Lin,et al. Gram matrix: an efficient representation of molecular conformation and learning objective for molecular pretraining[J]. BRIEFINGS IN BIOINFORMATICS,2024,25(4):13.
APA Xiang, Wenkai.,Zhong, Feisheng.,Ni, Lin.,Zheng, Mingyue.,Li, Xutong.,...&Wang, Dingyan.(2024).Gram matrix: an efficient representation of molecular conformation and learning objective for molecular pretraining.BRIEFINGS IN BIOINFORMATICS,25(4),13.
MLA Xiang, Wenkai,et al."Gram matrix: an efficient representation of molecular conformation and learning objective for molecular pretraining".BRIEFINGS IN BIOINFORMATICS 25.4(2024):13.

入库方式： OAI收割

来源：上海药物研究所

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