294 citations · 348 across the 16 of their papers we have counts for
16 papers
Multimodal Regression for Enzyme Turnover Rates Prediction
Bozhen Hu, Cheng Tan, Siyuan Li +4
The enzyme turnover rate is a fundamental parameter in enzyme kinetics, reflecting the catalytic efficiency of enzymes. However, enzyme turnover rates remain scarce across most org…
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…
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…
FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning
Sizhe Liu, Jun Xia, Lecheng Zhang +8
Molecular relational learning (MRL) is crucial for understanding the interaction behaviors between molecular pairs, a critical aspect of drug discovery and development. However, th…
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…
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…
A Graph is Worth Words: Euclideanizing Graph using Pure Transformer
Zhangyang Gao, Daize Dong, Cheng Tan +3
Can we model Non-Euclidean graphs as pure language or even Euclidean vectors while retaining their inherent information? The Non-Euclidean property have posed a long term challenge…
Deep Manifold Transformation for Protein Representation Learning
Bozhen Hu, Zelin Zang, Cheng Tan +1
Protein representation learning is critical in various tasks in biology, such as drug design and protein structure or function prediction, which has primarily benefited from protei…
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…
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…
Segment Anything in Defect Detection
Bozhen Hu, Bin Gao, Cheng Tan +2
Defect detection plays a crucial role in infrared non-destructive testing systems, offering non-contact, safe, and efficient inspection capabilities. However, challenges such as lo…
CCPL: Cross-modal Contrastive Protein Learning
Jiangbin Zheng, Stan Z. Li
Effective protein representation learning is crucial for predicting protein functions. Traditional methods often pretrain protein language models on large, unlabeled amino acid seq…
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…
Protein Language Models and Structure Prediction: Connection and Progression
Bozhen Hu, Jun Xia, Jiangbin Zheng +4
The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding. Recent advances have…
Generative De Novo Protein Design with Global Context
Cheng Tan, Zhangyang Gao, Jun Xia +2
The linear sequence of amino acids determines protein structure and function. Protein design, known as the inverse of protein structure prediction, aims to obtain a novel protein s…
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation
Jun Xia, Lirong Wu, Jintao Chen +2
Graph contrastive learning (GCL) has emerged as a dominant technique for graph representation learning which maximizes the mutual information between paired graph augmentations tha…