294 citations · 737 across the 61 of their papers we have counts for
62 papers
Tokenizing Electron Cloud in Protein-Ligand Interaction Learning
Haitao Lin, Odin Zhang, Jia Xu +6
The affinity and specificity of protein-molecule binding directly impact functional outcomes, uncovering the mechanisms underlying biological regulation and signal transduction. Mo…
dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen
Cheng Tan, Yijie Zhang, Zhangyang Gao +6
The development of therapeutic antibodies heavily relies on accurate predictions of how antigens will interact with antibodies. Existing computational methods in antibody design of…
A Simple yet Effective DDG Predictor is An Unsupervised Antibody Optimizer and Explainer
Lirong Wu, Yunfan Liu, Haitao Lin +4
The proteins that exist today have been optimized over billions of years of natural evolution, during which nature creates random mutations and selects them. The discovery of funct…
Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization
Lirong Wu, Haitao Lin, Yufei Huang +5
Antibodies are Y-shaped proteins that protect the host by binding to specific antigens, and their binding is mainly determined by the Complementary Determining Regions (CDRs) in th…
MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction
Cheng Tan, Zhenxiao Cao, Zhangyang Gao +6
Post-translational modifications (PTMs) profoundly expand the complexity and functionality of the proteome, regulating protein attributes and interactions that are crucial for biol…
FlowTS: Time Series Generation via Rectified Flow
Yang Hu, Xiao Wang, Zezhen Ding +7
Diffusion-based models have significant achievements in time series generation but suffer from inefficient computation: solving high-dimensional ODEs/SDEs via iterative numerical s…
Learning to Model Graph Structural Information on MLPs via Graph Structure Self-Contrasting
Lirong Wu, Haitao Lin, Guojiang Zhao +2
Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based on message passing to perform fea…
Teach Harder, Learn Poorer: Rethinking Hard Sample Distillation for GNN-to-MLP Knowledge Distillation
Lirong Wu, Yunfan Liu, Haitao Lin +2
To bridge the gaps between powerful Graph Neural Networks (GNNs) and lightweight Multi-Layer Perceptron (MLPs), GNN-to-MLP Knowledge Distillation (KD) proposes to distill knowledge…
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
Sitao Luan, Chenqing Hua, Qincheng Lu +11
Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of…
CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph
Haitao Lin, Guojiang Zhao, Odin Zhang +7
Structure-based drug design (SBDD) aims to generate potential drugs that can bind to a target protein and is greatly expedited by the aid of AI techniques in generative models. How…
UniIF: Unified Molecule Inverse Folding
Zhangyang Gao, Jue Wang, Cheng Tan +5
Molecule inverse folding has been a long-standing challenge in chemistry and biology, with the potential to revolutionize drug discovery and material science. Despite specified mod…
Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning
Lirong Wu, Yijun Tian, Haitao Lin +4
Protein-protein bindings play a key role in a variety of fundamental biological processes, and thus predicting the effects of amino acid mutations on protein-protein binding is cru…
PPFlow: Target-aware Peptide Design with Torsional Flow Matching
Haitao Lin, Odin Zhang, Huifeng Zhao +5
Therapeutic peptides have proven to have great pharmaceutical value and potential in recent decades. However, methods of AI-assisted peptide drug discovery are not fully explored.…
FoldToken: Learning Protein Language via Vector Quantization and Beyond
Zhangyang Gao, Cheng Tan, Jue Wang +3
Is there a foreign language describing protein sequences and structures simultaneously? Protein structures, represented by continuous 3D points, have long posed a challenge due to…
Advances of Deep Learning in Protein Science: A Comprehensive Survey
Bozhen Hu, Cheng Tan, Lirong Wu +7
Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological proces…
A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation
Lirong Wu, Haitao Lin, Zhangyang Gao +2
Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons (MLPs) re…
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks
Tianyu Fan, Lirong Wu, Yufei Huang +4
Recent years have witnessed the great success of graph pre-training for graph representation learning. With hundreds of graph pre-training tasks proposed, integrating knowledge acq…
Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions
Rui Sun, Lirong Wu, Haitao Lin +2
Augmentation is an effective alternative to utilize the small amount of labeled protein data. However, most of the existing work focuses on design-ing new architectures or pre-trai…
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding
Lirong Wu, Yijun Tian, Yufei Huang +4
Protein-Protein Interactions (PPIs) are fundamental in various biological processes and play a key role in life activities. The growing demand and cost of experimental PPI assays r…
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
Yufei Huang, Odin Zhang, Lirong Wu +5
Accurate prediction of protein-ligand binding structures, a task known as molecular docking is crucial for drug design but remains challenging. While deep learning has shown promis…
PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction
Lirong Wu, Yufei Huang, Cheng Tan +5
Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery. Existing deep learning-based me…
Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding
Bozhen Hu, Zelin Zang, Jun Xia +3
Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding. Most existing neural network approaches learn latent represent…
Masked Modeling for Self-supervised Representation Learning on Vision and Beyond
Siyuan Li, Luyuan Zhang, Zedong Wang +8
As the deep learning revolution marches on, self-supervised learning has garnered increasing attention in recent years thanks to its remarkable representation learning ability and…
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
Yufei Huang, Siyuan Li, Jin Su +9
Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimati…