About Me

I'm currently a Research Assistant Professor (Co-PI) at City University of Hong Kong (Dongguan), co-supervising the CityU-DG HANI Lab with Asst. Prof. Zhi-An Huang. Before that, I obtained my B.E. (2019), M.E. (2021), and Ph.D. (2025) degrees at the School of Intelligent Systems Engineering, Sun Yat-sen University, supervised by Prof. Calvin Yu-Chian Chen and Prof. Xiaojun Tan. I was a special research student (funded by CSC scholarship, 2023.9~2024.10) at the Laboratory of Functional Analysis in silico, Institute of Medical Science, University of Tokyo, supervised by Prof. Prof. Kenta Nakai. My research focuses on AI for life sciences, especially drug discovery, biomolecule generation, virtual cells, etc.

📧 Email: guanxing.chen@cityu-dg.edu.cn

📞 Phone: +86 137 1920 6018 (WeChat)

🏠 Address: City University of Hong Kong (Dongguan), Songshan Lake Zone, Dongguan, Guangdong 523808, China

🔍 Research Interests: AI for Life Sciences

News and Highlights

[2025.07] 🎉 Became a Research Assistant Professor at City University of Hong Kong (Dongguan)!

[2025.07] 📄 Paper published in Information Fusion on Modal-NexT for cellular data integration!

[2025.06] 🎓 Received the PhD degree from Sun Yat-sen University!

[2025.06] 📝 Review published in Physical Chemistry Chemical Physics on pre-trained language models for molecular design!

[2025.03] 🏆Received Shenzhen AI Natural Science Award as third contributor!

[2025.01] 📄 Paper accepted in npj Digital Medicine on dual modality drug-target affinity prediction!

[2024.12] 📄 Awarded National Scholarship for Doctoral Students for the second time!

[2024.10] 📄 Paper published in Nature Communications on modal-nexus auto-encoder for multi-modality integration!

[2024.09] 📄 Paper published in Journal of Chemical Theory and Computation on MMFA-DTA for COVID-19 drug repurposing!

[2024.08] 📄 Paper published in Nature Communications on SARS-CoV-2 antibody generation with LLMs!

More…

Latest Publications

* denotes equal contribution, † denotes corresponding author. Check out Publications page to learn more about my research!

[Information Fusion] Modal‑NexT: Toward unified heterogeneous cellular data integration. [Paper]
Z. Tang, G. Chen, S. Chen, H. He, J. Huang, T. Dong, J. Zhou, L. Zhao, L. You†, C.Y.‑C. Chen†

[Physical Chemistry Chemical Physics] Pre‑trained language models for protein and molecular design. [Paper]
E. Zhang, Z. Pan, Z. Yao, T. Dong, G. Chen, T. Deng, S. Chen, C.Y.‑C. Chen†

[npj Digital Medicine] Dual modality feature fused neural network integrating binding site information for DTA. [Paper]
H. He*, G. Chen*, Z. Tang, C.Y.‑C. Chen†

[IEEE TPAMI] Interaction‑Based Inductive Bias in Graph Neural Networks: Enhancing Protein‑Ligand Binding Affinity Predictions From 3D Structures. [Paper]
Z. Yang, W. Zhong, Q. Lv, T. Dong, G. Chen, C.Y.‑C. Chen†

[Journal of Chemical Theory and Computation] MMFA‑DTA: Multi‑Modal Feature Attention Fusion Network for Drug‑Target Affinity Prediction for Drug Repurposing Against SARS‑CoV‑2. [Paper]
G. Chen*, H. He*, Q. Lv*, L. Zhao, C.Y.‑C. Chen†

[Communications Biology] Knowledge‑Based Inductive Bias and Domain Adaptation: Enhancing Cell Type Annotation Across Batches. [Paper]
Z. Tang, G. Chen, S. Chen, H. He, L. You†, C.Y.‑C. Chen†

[Nature Communications] Modal‑nexus auto‑encoder for multi‑modality cellular data integration and imputation. [Paper]
Z. Tang, G. Chen, S. Chen, L. You†, C.Y.‑C. Chen†

[Nature Communications] De novo generation of SARS‑CoV‑2 antibody CDRH3 with a pre‑trained generative large language model. [Paper]
H. He, B. He†, L. Guan, Y. Zhao, F. Jiang, G. Chen, Q. Zhu, C.Y.-C. Chen†, T. Li†, J. Yao†

Research Highlights

My interdisciplinary research bridges AI and bioinformatics to advance drug discovery and precision medicine.

🧬 Multi-modal AI for Biology

Developing cutting-edge multi-modal fusion methods for integrating diverse biological data, including drug-target interactions, cellular data, and molecular representations.

💊 Drug Repurposing

Creating AI models to identify new therapeutic uses for existing drugs, with focus on COVID-19, cancer, and neurodegenerative diseases.

🌿 AI for Traditional Medicine

Building the world's largest TCM database (TCMBank) and developing AI methods to bridge traditional Chinese medicine with modern drug discovery.

🤖 Graph Neural Networks

Advancing GNN architectures for molecular property prediction, drug-drug interactions, and protein-ligand binding affinity prediction.

🏥 Clinical Applications

Translating AI research into clinical practice through collaborations on cancer diagnosis, biomarker discovery, and personalized treatment.

🔬 Protein Design

Leveraging transformer networks and language models for protein secondary structure prediction and antibody design.

Professional Services & Awards

🏆 Recent Awards

• Shenzhen AI Natural Science Award (2025)

• National Scholarship for Doctoral Students (2023, 2024)

• CSC Scholarship (2023)

📝 Research Service

Reviewer for: JCIM, iScience, IEEE TNNLS, Molecular Diversity, European Journal of Pharmacology, BMC Bioinformatics, Scientific Reports