44 citations · 113 across the 21 of their papers we have counts for
22 papers
BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature
Jiaxian Yan, Jintao Zhu, Yuhang Yang +8
Protein-ligand bioactivity data published in the literature are essential for drug discovery, yet manual curation struggles to keep pace with rapidly growing literature. Automated…
A Biosecurity Agent for Lifecycle LLM Biosecurity Alignment
Meiyin Meng, Zaixi Zhang
Large language models (LLMs) are increasingly integrated into biomedical research workflows--from literature triage and hypothesis generation to experimental design--yet this expan…
GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning
Rui Lv, Zaixi Zhang, Kai Zhang +6
Graph In-Context Learning, with the ability to adapt pre-trained graph models to novel and diverse downstream graphs without updating any parameters, has gained much attention in t…
DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking
Jiaxian Yan, Zaixi Zhang, Jintao Zhu +3
Molecular docking, a technique for predicting ligand binding poses, is crucial in structure-based drug design for understanding protein-ligand interactions. Recent advancements in…
FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling
Zaixi Zhang, Mengdi Wang, Qi Liu
Structure-based drug design (SBDD), which aims to generate 3D ligand molecules binding to target proteins, is a fundamental task in drug discovery. Existing SBDD methods typically…
Generalized Protein Pocket Generation with Prior-Informed Flow Matching
Zaixi Zhang, Marinka Zitnik, Qi Liu
Designing ligand-binding proteins, such as enzymes and biosensors, is essential in bioengineering and protein biology. One critical step in this process involves designing protein…
Towards Few-shot Self-explaining Graph Neural Networks
Jingyu Peng, Qi Liu, Linan Yue +3
Recent advancements in Graph Neural Networks (GNNs) have spurred an upsurge of research dedicated to enhancing the explainability of GNNs, particularly in critical domains such as…
FedGT: Federated Node Classification with Scalable Graph Transformer
Zaixi Zhang, Qingyong Hu, Yang Yu +2
Graphs are widely used to model relational data. As graphs are getting larger and larger in real-world scenarios, there is a trend to store and compute subgraphs in multiple local…
Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking
Jiaxian Yan, Zaixi Zhang, Kai Zhang +1
Molecular docking is a key computational tool utilized to predict the binding conformations of small molecules to protein targets, which is fundamental in the design of novel drugs…
Sparse Attention-Based Neural Networks for Code Classification
Ziyang Xiang, Zaixi Zhang, Qi Liu
Categorizing source codes accurately and efficiently is a challenging problem in real-world programming education platform management. In recent years, model-based approaches utili…
AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking
Yang Yu, Qi Liu, Kai Zhang +7
User modeling, which aims to capture users' characteristics or interests, heavily relies on task-specific labeled data and suffers from the data sparsity issue. Several recent stud…
Full-Atom Protein Pocket Design via Iterative Refinement
Zaixi Zhang, Zepu Lu, Zhongkai Hao +2
The design of \emph{de novo} functional proteins that bind specific ligand molecules is paramount in therapeutics and bio-engineering. A critical yet formidable task in this endeav…
Geometric Deep Learning for Structure-Based Drug Design: A Survey
Zaixi Zhang, Jiaxian Yan, Yining Huang +4
Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Traditional approaches, rooted in physicochemical mod…
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…
Backdoor Defense via Deconfounded Representation Learning
Zaixi Zhang, Qi Liu, Zhicai Wang +2
Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned examples in…
Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense
Yang Yu, Qi Liu, Likang Wu +3
Federated recommendation (FedRec) can train personalized recommenders without collecting user data, but the decentralized nature makes it susceptible to poisoning attacks. Most pre…
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…
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…