Bai Lab(Drug Design Lab)
Creating an efficient drug design framework by combining state-of-the-art computational methods with chemical biology technologies.
Research in the Bai Group aims to create an efficient drug design framework by combining state-of-the-art computational drug design methods with chemical biology technologies.
We are particularly interested in developing new computational biology and drug design methods by introducing artificial intelligence algorithms, such as protein-protein interaction prediction, peptide design, molecular degrader design, and selective covalent inhibitor design. Using these methods, we explore the molecular mechanisms involved in the development of serious diseases, such as cancer, infectious diseases, and metabolic diseases, and develop therapeutic agents accordingly.
To date, we have developed a number of computational drug design programs, most of which are freely available on our website for academic use. Using these methods, we have successfully designed several highly bioactive compounds or peptides. Some of them are being studied in detail in preclinical phases.
Key Research Areas
Protein–Protein Interaction Prediction and Modulation
We develop AI- and physics-based methods for predicting and modulating protein–protein interactions, with a focus on discovering unknown PPI pairs, identifying druggable interaction sites, and rationally designing PPI stabilizers and disruptors.
PROTAC Molecular Design
We are committed to continuously developing new computational methods for PROTAC molecular design, including linker design, de novo generation, degradation efficacy prediction, and druggability assessment, complemented by molecular simulations to elucidate PROTAC-mediated degradation mechanisms.
Molecular Glue Design
We are dedicated to developing systematic strategies for molecular glue discovery and design, integrating AI, structural biology, and molecular simulations to elucidate their mechanisms of action and enable de novo generation, activity prediction, and molecular optimization.
Innovative Drug Discovery and Translational Applications
We apply our computational methods and platforms to innovative drug discovery for cancer, viral infections, and autoimmune diseases, spanning target identification, lead optimization, and efficacy and druggability evaluation.
Fang Bai, Ph.D.
Principal InvestigatorAssociate Professor, School of Life Science and Technology; Research Professor, Shanghai Institute for Advanced Immunochemical Studies; Visiting Professor, School of Information Science and Technology, ShanghaiTech University.
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Acknowledgement
The Bai Lab is supported by the National Natural Science Foundation of China, the Ministry of Science and Technology, the Shanghai Science and Technology Commission, the Shanghai Science and Technology Development Funds, and the Start-up Package of ShanghaiTech University. Most computational drug design programs developed in our lab are freely available for academic use.