1 code implementation • 10 Nov 2024 • Chenqing Hua, Jiarui Lu, Yong liu, Odin Zhang, Jian Tang, Rex Ying, Wengong Jin, Guy Wolf, Doina Precup, Shuangjia Zheng
Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex.
no code implementations • 9 Nov 2024 • Chenqing Hua
We introduce a GNN equipped with an advanced high-order pooling function, adept at capturing complex node interactions within graph-structured data.
1 code implementation • 1 Oct 2024 • Chenqing Hua, Yong liu, Dinghuai Zhang, Odin Zhang, Sitao Luan, Kevin K. Yang, Guy Wolf, Doina Precup, Shuangjia Zheng
Enzyme design is a critical area in biotechnology, with applications ranging from drug development to synthetic biology.
no code implementations • 9 Sep 2024 • Sitao Luan, Qincheng Lu, Chenqing Hua, Xinyu Wang, Jiaqi Zhu, Xiao-Wen Chang, Guy Wolf, Jian Tang
To overcome these challenges, we first train and fine-tune baseline models on $27$ most widely used benchmark datasets, categorize them into three distinct groups: malignant, benign and ambiguous heterophilic datasets, and identify the real challenging subsets of tasks.
2 code implementations • 24 Aug 2024 • Chenqing Hua, Bozitao Zhong, Sitao Luan, Liang Hong, Guy Wolf, Doina Precup, Shuangjia Zheng
Enzymes, with their specific catalyzed reactions, are necessary for all aspects of life, enabling diverse biological processes and adaptations.
no code implementations • 12 Jul 2024 • Sitao Luan, Chenqing Hua, Qincheng Lu, Liheng Ma, Lirong Wu, Xinyu Wang, Minkai Xu, Xiao-Wen Chang, Doina Precup, Rex Ying, Stan Z. Li, Jian Tang, Guy Wolf, Stefanie Jegelka
In this survey, we provide a comprehensive review of the latest progress on heterophilic graph learning, including an extensive summary of benchmark datasets and evaluation of homophily metrics on synthetic graphs, meticulous classification of the most updated supervised and unsupervised learning methods, thorough digestion of the theoretical analysis on homophily/heterophily, and broad exploration of the heterophily-related applications.
no code implementations • 15 Mar 2024 • Odin Zhang, Yufei Huang, Shichen Cheng, Mengyao Yu, Xujun Zhang, Haitao Lin, Yundian Zeng, Mingyang Wang, Zhenxing Wu, Huifeng Zhao, Zaixi Zhang, Chenqing Hua, Yu Kang, Sunliang Cui, Peichen Pan, Chang-Yu Hsieh, Tingjun Hou
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets.
no code implementations • 6 Feb 2024 • Chenqing Hua, Connor Coley, Guy Wolf, Doina Precup, Shuangjia Zheng
Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense.
no code implementations • 28 Apr 2023 • Chenqing Hua, Sitao Luan, Minkai Xu, Rex Ying, Jie Fu, Stefano Ermon, Doina Precup
Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.
1 code implementation • 25 Apr 2023 • Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Jie Fu, Jure Leskovec, Doina Precup
Homophily principle, i. e., nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority of Graph Neural Networks (GNNs) over Neural Networks on node classification tasks.
no code implementations • 21 Dec 2022 • Sitao Luan, Mingde Zhao, Chenqing Hua, Xiao-Wen Chang, Doina Precup
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood information of nodes.
no code implementations • 30 Oct 2022 • Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Doina Precup
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by additionally making use of graph structure based on the relational inductive bias (edge bias), rather than treating the nodes as collections of independent and identically distributed (i. i. d.)
1 code implementation • 14 Oct 2022 • Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup
ACM is more powerful than the commonly used uni-channel framework for node classification tasks on heterophilic graphs and is easy to be implemented in baseline GNN layers.
Inductive Bias
Node Classification on Non-Homophilic (Heterophilic) Graphs
no code implementations • 24 May 2022 • Chenqing Hua, Sitao Luan, Qian Zhang, Jie Fu
Graph Neural Networks (GNNs) are new inference methods developed in recent years and are attracting growing attention due to their effectiveness and flexibility in solving inference and learning problems over graph-structured data.
no code implementations • 24 May 2022 • Chenqing Hua, Guillaume Rabusseau, Jian Tang
Graph Neural Networks (GNNs) are attracting growing attention due to their effectiveness and flexibility in modeling a variety of graph-structured data.
no code implementations • 29 Sep 2021 • Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
no code implementations • 12 Sep 2021 • Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
Ranked #1 on
Node Classification
on Citeseer
no code implementations • NeurIPS 2021 • Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
no code implementations • 20 Aug 2020 • Sitao Luan, Mingde Zhao, Chenqing Hua, Xiao-Wen Chang, Doina Precup
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood node information.