Computational Biomolecule Discovery
Biomolecules are the building blocks for all life processes and the cornerstones for modern catalytic and pharmaceutical industry. The insights into their interactions and chemical transformations are essential for developing new drugs, biocatalysts, and therapeutic strategies. Enabled by the rapid evolution of computing power and data science algorithms, simulating the structure, dynamics, and chemical functions of biomolecules are now faster and more accurate than ever. This grants us a special opportunity to innovate the high-throughput computational protocols to accelerate the design and discovery of functional biomolecules. My research group focuses on integrating first-principles simulation and data-driven modeling to automatically evaluate, understand, and design functional biomolecules for catalytic and biomedical applications.
Missions of Data-Driven Modeling:
- Develop new database to integrate enzyme structure, kinetics, and atomistic data in one place.
- Develop new machine learning models to efficiently design protein mutation for functional enhancement.
Missions of Physics-Based Modeling:
- Develop an automatic high-throughput workflow for multiscale enzyme atomistic modeling.
- Develop new first-principles simulation protocols to evaluate the thermodynamic and kinetic parameters for enzyme catalysis and constrained peptide machine.
What we are doing: