10 citations · 52 across the 12 of their papers we have counts for
12 papers
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…
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…
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…