Qifeng Bai

School of Basic Medical Sciences
Lanzhou University
Lanzhou 730000, Gansu, P. R. China
Email: molaical@yeah.net
ORCID: https://orcid.org/0000-0001-7296-6187
Google Scholar: https://scholar.google.com/citations?user=7w7Vwh0AAAAJ
ResearchGate: https://www.researchgate.net/profile/Qifeng-Bai
Homepage in Lanzhou University: jchyxyen.lzu.edu.cn

Note: my homepage will be continuously updated in this page.

欢迎加入我们,长期接收保送研究生或报考研究生专业: 兰州大学基础医学院 "生物化学与分子生物学"

Research interests

1) Artificial intelligence for drug design including deep learning, machine learning, etc
2) Software development for drug design such as MolAICal (https://molaical.github.io or https://molaical.gitee.io)
3) Molecular dynamics simulations
4) Studying the biological mechanism of proteins and ligands by molecular dynamics simulations, machine learning and deep learning methods, QM/MM, etc
5) Bioinformatics, cheminformatics, biomedical informatics, and computational biology
6) Drug design of kinases, G protein-coupled receptors, and so on

Selected publications (#: Co-first; *: Co-corresponding)

[3]. Bai, Q.*, Liu, S., Tian, Y., Xu, T.*, Banegas-Luna, A. J., Pérez-Sánchez, H.*, et al. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. WIREs Comput Mol Sci. 2021;e1581. https://doi.org/10.1002/wcms.1581
[2]. Bai, Q.*, Ma, J., Liu, S., Xu, T., Banegas-Luna, A. J., Pérez-Sánchez, H.*, et al. WADDAICA: A webserver for aiding protein drug design by artificial intelligence and classical algorithm. Computational and Structural Biotechnology Journal 19, 3573-3579, (2021). https://doi.org/10.1016/j.csbj.2021.06.017
[1]. Bai, Q.*, et al. MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm. Briefings in bioinformatics 2021, 22, bbaa161. https://doi.org/10.1093/bib/bbaa161


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