Qifeng Bai
Professor
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.
Welcome to join us, receiving recommended or exam-exempted Chinese postgraduates, or applying for postgraduates. Major: Biochemistry and Molecular Biology, School of basic medicine, Lanzhou University.
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