Search Results for author: Chenqing Hua

Found 19 papers, 5 papers with code

Reaction-conditioned De Novo Enzyme Design with GENzyme

1 code implementation10 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.

Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning

no code implementations9 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.

Graph Representation Learning Knowledge Graphs

Are Heterophily-Specific GNNs and Homophily Metrics Really Effective? Evaluation Pitfalls and New Benchmarks

no code implementations9 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.

ReactZyme: A Benchmark for Enzyme-Reaction Prediction

2 code implementations24 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.

The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges

no code implementations12 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.

Graph Learning Graph Representation Learning

Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation

no code implementations15 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.

Graph Generation

Effective Protein-Protein Interaction Exploration with PPIretrieval

no code implementations6 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.

MUDiff: Unified Diffusion for Complete Molecule Generation

no code implementations28 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.

3D geometry Drug Discovery +1

When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability

1 code implementation25 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.

Node Classification Stochastic Block Model

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks

no code implementations21 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.

Node Classification

When Do We Need Graph Neural Networks for Node Classification?

no code implementations30 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.)

Classification Inductive Bias +1

Revisiting Heterophily For Graph Neural Networks

1 code implementation14 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

Graph Neural Networks Intersect Probabilistic Graphical Models: A Survey

no code implementations24 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.

Survey

High-Order Pooling for Graph Neural Networks with Tensor Decomposition

no code implementations24 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.

Graph Classification Graph Neural Network +3

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks

no code implementations20 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.

Graph Classification Node Classification

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