Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing
文献类型：期刊论文
| 作者 | Wang, Yusong5,6,7; Wang, Tong5; Li, Shaoning5; He, Xinheng1,2,4,5; Li, Mingyu3,5; Wang, Zun5; Zheng, Nanning6,7; Shao, Bin5; Liu, Tie-Yan5 |
| 刊名 | NATURE COMMUNICATIONS
 |
| 出版日期 | 2024-01-05 |
| 卷号 | 15期号:1页码:13 |
| DOI | 10.1038/s41467-023-43720-2 |
| 通讯作者 | Wang, Tong(watong@microsoft.com) ; Shao, Bin(binshao@microsoft.com) |
| 英文摘要 | Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures. Utilising geometric information and reducing computational costs are key challenges in the molecular modelling field. Here, authors propose ViSNet, which efficiently extracts geometric features, accurately predicts molecular properties, and drives simulations with interpretability. |
| WOS关键词 | EQUATIONS ; EXCHANGE |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| 出版者 | NATURE PORTFOLIO |
| WOS记录号 | WOS:001142908000016 |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/308793]  |
| 专题 | 中国科学院上海药物研究所 |
| 通讯作者 | Wang, Tong; Shao, Bin |
| 作者单位 | 1.Chinese Acad Sci, Shanghai Inst Mat Med, CAS Key Lab Receptor Res, Shanghai 201203, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China 3.Shanghai Jiao Tong Univ, Med Chem & Bioinformat Ctr, Sch Med, Shanghai 200025, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Microsoft Res AI4Science, Beijing 100080, Peoples R China 6.Xi An Jiao Tong Univ, Natl Key Lab Human Machine Hybrid Augmented Intell, Natl Engn Res Ctr Visual Informat & Applicat, Xian 710049, Peoples R China 7.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian 710049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yusong,Wang, Tong,Li, Shaoning,et al. Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing[J]. NATURE COMMUNICATIONS,2024,15(1):13. |
| APA | Wang, Yusong.,Wang, Tong.,Li, Shaoning.,He, Xinheng.,Li, Mingyu.,...&Liu, Tie-Yan.(2024).Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.NATURE COMMUNICATIONS,15(1),13. |
| MLA | Wang, Yusong,et al."Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing".NATURE COMMUNICATIONS 15.1(2024):13. |
入库方式： OAI收割
来源：上海药物研究所
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