SubGDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning

9 May 2024

Papers citing "SubGDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning"

6 / 6 papers shown

Title
Diffusion Models for Graphs Benefit From Discrete State Spaces K. Haefeli Karolis Martinkus Nathanael Perraudin Roger Wattenhofer DiffM 71 51 0 04 Oct 2022
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast Yuyang Wang Rishikesh Magar Chen Liang A. Farimani 32 78 0 18 Feb 2022
Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks Zhaoning Yu Hongyang Gao 21 38 0 01 Feb 2022
Pre-training Molecular Graph Representation with 3D Geometry Shengchao Liu Hanchen Wang Weiyang Liu Joan Lasenby Hongyu Guo Jian Tang 106 294 0 07 Oct 2021
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs Zhao Xu Youzhi Luo Xuan Zhang Xinyi Xu Yaochen Xie Meng Liu Kaleb Dickerson Cheng Deng Maho Nakata Shuiwang Ji 33 39 0 30 Sep 2021
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 152 1,748 0 02 Mar 2017