As a computational chemist, I aim to accelerate reaction discovery by understanding mechanisms and leveraging data science for data-driven approaches. My work focuses on three main areas:

1. Transition Metal Catalysis

   • Mechanistic Insights and Reaction Design
     Collaborated with leading chemists like Keary Engle, Huw Davies, Steven Buchwald, and Vladimir Gevorgyan on various transition metal-catalyzed reactions. Used computational tools to understand factors affecting reactivity and selectivity, including directing group and ligand design.
   • Predictive Modelling
     Developed parameters for predicting selectivities and reactivities in directed alkene functionalizations, working with small datasets derived from reaction mechanisms.

2. Cheminformatics and Data Science

   • HeteroAryl Descriptors (HArD) Database
     Created a database of ~31,000 monosubstituted heteroarenes featuring over 60 descriptors (e.g., heteroaryl Hammett parameters, aromaticity parameters, HOMO-LUMO gaps, buried volumes).
   • User-Friendly Tools and Documentation
     Developed a web interface allowing users to draw heteroaryls and obtain descriptors for different regioisomers. Provided detailed documentation on dataset generation, from SMILES extraction using Reaxys to high-throughput DFT calculations.

3. Enzyme Catalysis with Prof. Yang Yang

   • Directed Evolution Optimization
     Recently, I have been working on identifying target amino acids for mutation in directed evolution experiments involving up to 900 mutations. I am using existing amino acid parameters and trying to develop molecular dynamics (MD)-based parameters to quantify features like loop flexibility.
   • Computational Structural Studies
     Conducted classical MD and QM/MM simulations to gain structural insights, aiding in the engineering of enzymes for highly stereoselective synthesis.