I am a postdoctoral researcher at the University of Florida, working in computational drug discovery and molecular design. My research combines computational chemistry, computational biology, and artificial intelligence to accelerate protein engineering and small-molecule drug discovery.
My work focuses on important therapeutic targets and explores their sequence, structure, and function, with particular interest in small-molecule binding, catalytic mechanisms, and computational workflows that closely connect theory with chemical and biological experiments.
My current research interests include generative AI for de novo protein design, virtual screening and molecular design based on CADD and AIDD, and molecular mechanism studies of enzymes and GPCRs using molecular simulations. More details are available on the lab website and my Google Scholar profile.
🔥 News
- 2025: Our work on generative-AI-designed haloalkane dehalogenase variants was published in Journal of the American Chemical Society.
- 2024: Our perspective article “Computational drug development for membrane protein targets” was published in Nature Biotechnology.
- 2024: Our work on machine-learning-based kinetic and thermodynamic prediction was published in Journal of Chemical Theory and Computation.
- 2024: Our perspective “Will the hype of automated drug discovery finally be realized?” was published in Expert Opinion on Drug Discovery.
📝 Publications
Natalia Gelfand, Vojtech Orel, Wenqiang Cui, Jiri Damborsky, Chenglong Li, Zbynek Prokop, Wen Jun Xie, Arieh Warshel
- A representative study on using generative AI to support enzyme and protein design with combined biochemical and computational validation.
Computational Drug Development for Membrane Protein Targets
Haijian Li, Xiaolin Sun, Wenqiang Cui, Marc Xu, Junlin Dong, Babatunde Edukpe Ekundayo, Dongchun Ni, Zhili Rao, Liwei Guo, Henning Stahlberg, Shuguang Yuan, Horst Vogel
- A broad perspective on computational strategies for membrane-protein-targeted drug development, integrating structure, simulation, and practical discovery workflows.
Junlin Dong, Shiyu Wang, Wenqiang Cui, Xiaolin Sun, Haojie Guo, Hailu Yan, Horst Vogel, Zhi Wang, Shuguang Yuan
- A machine-learning framework for accurate kinetic and thermodynamic prediction in molecular mechanism studies.
- Will the Hype of Automated Drug Discovery Finally Be Realized?, Wenqiang Cui, Shuguang Yuan, Expert Opinion on Drug Discovery, 2024
- Molecular Basis of Ligand Selectivity for Melatonin Receptors, Wenqiang Cui, Junlin Dong, Shiyu Wang, Horst Vogel, Rongfeng Zou, Shuguang Yuan, RSC Advances, 2023
🧪 Research Directions
- Generative AI for de novo protein design: applying deep learning and generative models to learn from natural proteins and accelerate enzyme engineering.
- CADD and AIDD for small-molecule design: developing and applying virtual screening, docking, free-energy, and machine-learning methods for target-ligand interaction prediction.
- Molecular simulations of enzymes and GPCRs: studying catalytic mechanisms, ligand selectivity, and structure-function relationships with simulation-guided workflows.
📖 Background
- Current: Professor, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
- Research areas: computational chemistry, computational biology, artificial intelligence, protein design, and drug discovery.
- Group profile: We welcome undergraduate students, graduate students, and postdoctoral researchers interested in interdisciplinary research at the interface of AI, chemistry, and biology.
📬 Contact
- Lab website: https://wqcui-lab.github.io/
- Research directions: https://wqcui-lab.github.io/research
- Google Scholar: https://scholar.google.com/citations?hl=en&user=neci0IQAAAAJ
- Email: you can add your direct contact email later in
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