4 papers
Revealing the Pitfalls and Re-Evaluating the Advancement of Heterophilic Graph Learning
Sitao Luan, Qincheng Lu, Chenqing Hua +3
Over the past decade, Graph Neural Networks (GNNs) have achieved great success on machine learning tasks with relational data. However, recent studies have found that heterophily c…
Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning
Chenqing Hua
Graphs serve as fundamental descriptors for systems composed of interacting elements, capturing a wide array of data types, from molecular interactions to social networks and knowl…
EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics
Chenqing Hua, Yong Liu, Dinghuai Zhang +6
Enzyme design is a critical area in biotechnology, with applications ranging from drug development to synthetic biology. Traditional methods for enzyme function prediction or prote…
ReactZyme: A Benchmark for Enzyme-Reaction Prediction
Chenqing Hua, Bozitao Zhong, Sitao Luan +4
Enzymes, with their specific catalyzed reactions, are necessary for all aspects of life, enabling diverse biological processes and adaptations. Predicting enzyme functions is essen…