Guowei Zhou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Guowei Zhou has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Song He
Academy of Military Medical Sciences
2 shared publications - Xiaochen Bo
Academy of Military Medical Sciences, Beijing 100850, China
2 shared publications - Yanpeng Zhao
School of Medicine, Shanghai University, Shanghai, China
2 shared publications - Baoqing Wang
Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China
1 shared publication - Jingjing Wang
School of Environmental and Chemical Engineering, Shanghai University, Shanghai, China
1 shared publication - Naihan Shi
Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China
1 shared publication
Latest Publications
- 3 August 2026
- Published by Springer Nature in Molecular Diversity
- Vol. 30 (6) , 10473-10486
CBInformax: bioactivity-aware self-supervised molecular representation learning for molecular property and drug–drug interaction prediction
- 3 August 2026
- Published by Springer Nature in Molecular Diversity
- Vol. 30 (6) , 10473-10486
CBInformax: bioactivity-aware self-supervised molecular representation learning for molecular property and drug–drug interaction prediction
- Open Access
- 19 March 2025
- Published by MDPI AG in International Journal of Molecular Sciences
- Vol. 26 (6) , 2783
SST-ResNet: A Sequence and Structure Information Integration Model for Protein Property Prediction
Proteins are the basic building blocks of life and perform fundamental functions in biology. Predicting protein properties based on amino acid sequences and 3D structures has become a key approach to accelerating drug development. In this study, we propose a novel sequence- and structure-based framework, SST-ResNet, which consists of the multimodal language model ProSST and a multi-scale information integration module. This framework is designed to deeply explore the latent relationships between protein sequences and structures, thereby achieving superior synergistic prediction performance. Our method outperforms previous joint prediction models on Enzyme Commission (EC) numbers and Gene Ontology (GO) tasks. Furthermore, we demonstrate the necessity of multi-scale information integration for these two types of data and illustrate its exceptional performance on key tasks. We anticipate that this framework can be extended to a broader range of protein property prediction problems, ultimately facilitating drug development.
- 19 March 2025
- Published by MDPI AG in International Journal of Molecular Sciences
- Vol. 26 (6) , 2783
Open AccessSST-ResNet: A Sequence and Structure Information Integration Model for Protein Property Prediction
Proteins are the basic building blocks of life and perform fundamental functions in biology. Predicting protein properties based on amino acid sequences and 3D structures has become a key approach to accelerating drug development. In this study, we propose a novel sequence- and structure-based framework, SST-ResNet, which consists of the multimodal language model ProSST and a multi-scale information integration module. This framework is designed to deeply explore the latent relationships between protein sequences and structures, thereby achieving superior synergistic prediction performance. Our method outperforms previous joint prediction models on Enzyme Commission (EC) numbers and Gene Ontology (GO) tasks. Furthermore, we demonstrate the necessity of multi-scale information integration for these two types of data and illustrate its exceptional performance on key tasks. We anticipate that this framework can be extended to a broader range of protein property prediction problems, ultimately facilitating drug development.