10 citations · 50 across the 23 of their papers we have counts for
25 papers
VecFormer: Towards Efficient and Generalizable Graph Transformer with Graph Token Attention
Jingbo Zhou, Jun Xia, Siyuan Li +11
Graph Transformer has demonstrated impressive capabilities in the field of graph representation learning. However, existing approaches face two critical challenges: (1) most models…
Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schrödinger Bridges
Changxi Chi, Yufei Huang, Jun Xia +4
Predicting single-cell perturbation outcomes directly advances gene function analysis and facilitates drug candidate selection, making it a key driver of both basic and translation…
AlphaFold Database Debiasing for Robust Inverse Folding
Cheng Tan, Zhenxiao Cao, Zhangyang Gao +3
The AlphaFold Protein Structure Database (AFDB) offers unparalleled structural coverage at near-experimental accuracy, positioning it as a valuable resource for data-driven protein…
Doloris: Dual Conditional Diffusion Implicit Bridges with Sparsity Masking Strategy for Unpaired Single-Cell Perturbation Estimation
Changxi Chi, Jun Xia, Yufei Huang +9
Estimating single-cell responses across various perturbations facilitates the identification of key genes and enhances drug screening, significantly boosting experimental efficienc…
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…
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…
GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models
Zicheng Liu, Jiahui Li, Siyuan Li +5
The Genomic Foundation Model (GFM) paradigm is expected to facilitate the extraction of generalizable representations from massive genomic data, thereby enabling their application…
VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling
Siyuan Li, Zedong Wang, Zicheng Liu +5
Similar to natural language models, pre-trained genome language models are proposed to capture the underlying intricacies within genomes with unsupervised sequence modeling. They h…
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…
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
Odin Zhang, Yufei Huang, Shichen Cheng +14
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pock…
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…
Progressive Multi-Modality Learning for Inverse Protein Folding
Jiangbin Zheng, Stan Z. Li
While deep generative models show promise for learning inverse protein folding directly from data, the lack of publicly available structure-sequence pairings limits their generaliz…