no code implementations • 8 Mar 2024 • Bozhen Hu, Cheng Tan, Lirong Wu, Jiangbin Zheng, Jun Xia, Zhangyang Gao, Zicheng Liu, Fandi Wu, Guijun Zhang, Stan Z. Li
Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological processes.
1 code implementation • 13 Feb 2024 • Lirong Wu, Yufei Huang, Cheng Tan, Zhangyang Gao, Bozhen Hu, Haitao Lin, Zicheng Liu, Stan Z. Li
Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery.
no code implementations • 4 Feb 2024 • Zhangyang Gao, Daize Dong, Cheng Tan, Jun Xia, Bozhen Hu, Stan Z. Li
Despite recent GNN and Graphformer efforts encoding graphs as Euclidean vectors, recovering original graph from the vectors remains a challenge.
no code implementations • 12 Jan 2024 • Bozhen Hu, Zelin Zang, Cheng Tan, Stan Z. Li
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 protein language models and graph neural networks.
no code implementations • 12 Jan 2024 • Bozhen Hu, Zelin Zang, Jun Xia, Lirong Wu, Cheng Tan, Stan Z. Li
Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding.
1 code implementation • 11 Dec 2023 • Jiangbin Zheng, Siyuan Li, Yufei Huang, Zhangyang Gao, Cheng Tan, Bozhen Hu, Jun Xia, Ge Wang, Stan Z. Li
Protein design involves generating protein sequences based on their corresponding protein backbones.
no code implementations • 17 Nov 2023 • Bozhen Hu, Bin Gao, Cheng Tan, Tongle Wu, Stan Z. Li
Defect detection plays a crucial role in infrared non-destructive testing systems, offering non-contact, safe, and efficient inspection capabilities.
1 code implementation • 21 Apr 2023 • Cheng Tan, Zhangyang Gao, Lirong Wu, Jun Xia, Jiangbin Zheng, Xihong Yang, Yue Liu, Bozhen Hu, Stan Z. Li
In this paper, we propose a \textit{simple yet effective} model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner.
no code implementations • 19 Mar 2023 • Jiangbin Zheng, Ge Wang, Yufei Huang, Bozhen Hu, Siyuan Li, Cheng Tan, Xinwen Fan, Stan Z. Li
In this work, we introduce a novel unsupervised protein structure representation pretraining with a robust protein language model.
1 code implementation • 25 Jan 2023 • Cheng Tan, Yijie Zhang, Zhangyang Gao, Bozhen Hu, Siyuan Li, Zicheng Liu, Stan Z. Li
We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure.
1 code implementation • 30 Nov 2022 • Bozhen Hu, Jun Xia, Jiangbin Zheng, Cheng Tan, Yufei Huang, Yongjie Xu, Stan Z. Li
The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding.
1 code implementation • 21 Apr 2022 • Cheng Tan, Zhangyang Gao, Jun Xia, Bozhen Hu, Stan Z. Li
Thus, we propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules.
1 code implementation • 7 Feb 2022 • Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, Stan Z. Li
Furthermore, we devise adversarial training scheme, dubbed \textbf{AT-SimGRACE}, to enhance the robustness of graph contrastive learning and theoretically explain the reasons.