Professor Qifeng Bai PhD

Professor Qifeng Bai is a distinguished computational scientist specializing in Artificial Intelligence (AI) for Drug Design and Computational Biology. His research centers on integrating deep learning, machine learning, and molecular dynamics simulation to accelerate new drug discovery. He is the lead developer of the MolAICal software tool, which utilizes AI and classical algorithms for 3D drug design. Dr. Bai's expertise in Biomedical Informatics and Cheminformatics focuses on studying the biological mechanisms of proteins and ligands to promote efficient pharmaceutical research.

Professional Background:

• Professor (Current, Since 2022) - School Of Basic Medical Sciences, Lanzhou University.
   

Achievements:

• Lead Developer of MolAICal software for 3D drug design.
• Associate Editor for Frontiers In Chemistry (Medicinal And Pharmaceutical Chemistry).
   

Current Research Projects:

• MolAICal Software Development: Continuously developing and improving the software for 3D structure-based drug design using deep learning.
• Antibody And Nanobody Design: Utilizing adaptive autoregressive diffusion approaches for designing active humanized antibodies.
   

Academic Profiles of Professor Qifeng Bai

Explore his academic and professional presence across trusted platforms:

• University Profile
• Google Scholar
• ResearchGate
• LinkedIn

Professor Bai's publications demonstrate a strong command of integrating cutting-edge deep learning techniques with computational biology to revolutionize the drug discovery process.

The list of recent publications of Professor Qifeng Bai is listed below:

• Sun, Y., Qian, X., Xu, W., Zhang, H., Xiao, C., Li, L., Rong, Y., Huang, W., Bai, Q., & Xu, T. (2025). ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning. Journal article. DOI: 10.48550/ARXIV.2506.09513.
• Zhang, P., Liu, M., Pei, S., Huang, H., Zhao, Z., Yang, L., Pan, W., Li, S., Bai, Q., & Zhang, R., et al. (2025). Efficient Differentiation of hiPSCs into hMSC-like Cells under Chemically Defined Conditions on Temperature-Sensitive Micropatterned Surfaces. ACS Applied Materials & Interfaces. DOI: 10.1021/acsami.4c13686.
• Bai, Q., Xu, T., Huang, J., & Pérez-Sánchez, H. (2024). Geometric deep learning methods and applications in 3D structure-based drug design. Drug Discovery Today. DOI: 10.1016/j.drudis.2024.104024.
• Ma, J., Wu, F., Xu, T., Xu, S., Liu, W., Yan, D., Bai, Q., & Yao, J. (2024). An adaptive autoregressive diffusion approach to design active humanized antibody and nanobody. Preprint. DOI: 10.1101/2024.10.22.619416.
• Bai, Q., Ma, J., & Xu, T. (2024). AI Deep Learning Generative Models for Drug Discovery. Applications Of Generative AI (Book chapter). DOI: 10.1007/978-3-031-46238-2_23.

Last Updated on October 09, 2025