67 citations · 220 across the 37 of their papers we have counts for
6 papers · 1 filter
PFMBench: Protein Foundation Model Benchmark
Zhangyang Gao, Hao Wang, Cheng Tan +6
This study investigates the current landscape and future directions of protein foundation model research. While recent advancements have transformed protein science and engineering…
FoldToken2: Learning compact, invariant and generative protein structure language
Zhangyang Gao, Cheng Tan, Stan Z. Li
The equivalent nature of 3D coordinates has posed long term challenges in protein structure representation learning, alignment, and generation. Can we create a compact and invarian…
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…
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…
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
Cheng Tan, Yijie Zhang, Zhangyang Gao +4
While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA se…