281 citations · 455 across the 11 of their papers we have counts for
12 papers
Learning Subpocket Prototypes for Generalizable Structure-based Drug Design
Zaixi Zhang, Qi Liu
Generating molecules with high binding affinities to target proteins (a.k.a. structure-based drug design) is a fundamental and challenging task in drug discovery. Recently, deep ge…
An Equivariant Generative Framework for Molecular Graph-Structure Co-Design
Zaixi Zhang, Qi Liu, Chee-Kong Lee +2
Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine…
Hierarchical Graph Transformer with Adaptive Node Sampling
Zaixi Zhang, Qi Liu, Qingyong Hu +1
The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision. However, when it comes to graph-struc…
Model Inversion Attacks against Graph Neural Networks
Zaixi Zhang, Qi Liu, Zhenya Huang +3
Many data mining tasks rely on graphs to model relational structures among individuals (nodes). Since relational data are often sensitive, there is an urgent need to evaluate the p…
Evaluating Self-Supervised Learning for Molecular Graph Embeddings
Hanchen Wang, Jean Kaddour, Shengchao Liu +3
Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular gra…
Deep Unified Representation for Heterogeneous Recommendation
Chengqiang Lu, Mingyang Yin, Shuheng Shen +3
Recommendation system has been a widely studied task both in academia and industry. Previous works mainly focus on homogeneous recommendation and little progress has been made for…
Graph Self-Supervised Learning for Optoelectronic Properties of Organic Semiconductors
Zaixi Zhang, Qi Liu, Shengyu Zhang +3
The search for new high-performance organic semiconducting molecules is challenging due to the vastness of the chemical space, machine learning methods, particularly deep learning…
ProtGNN: Towards Self-Explaining Graph Neural Networks
Zaixi Zhang, Qi Liu, Hao Wang +2
Despite the recent progress in Graph Neural Networks (GNNs), it remains challenging to explain the predictions made by GNNs. Existing explanation methods mainly focus on post-hoc e…
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
Zaixi Zhang, Qi Liu, Hao Wang +2
Predicting molecular properties with data-driven methods has drawn much attention in recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable success i…
Deep Learning for Optoelectronic Properties of Organic Semiconductors
Chengqiang Lu, Qi Liu, Qiming Sun +4
Atomistic modeling of energetic disorder in organic semiconductors (OSCs) and its effects on the optoelectronic properties of OSCs requires a large number of excited-state electron…
Hyperbolic Graph Neural Networks
Qi Liu, Maximilian Nickel, Douwe Kiela
Learning from graph-structured data is an important task in machine learning and artificial intelligence, for which Graph Neural Networks (GNNs) have shown great promise. Motivated…
Quaternion Knowledge Graph Embeddings
Shuai Zhang, Yi Tay, Lina Yao +1
In this work, we move beyond the traditional complex-valued representations, introducing more expressive hypercomplex representations to model entities and relations for knowledge…