Computational Protein Engineering & Deep Learning
My research integrates molecular simulations with deep learning to engineer proteins or design small molecule therapeutics. Current and past projects include:
Data-driven physical CVs for enhanced sampling
Developing new physics-based collective variables that can be applied to any protein with your favorite enhanced sampling method.
General principles of allostery in proteins
Using molecular simulations and protein-agnostic CVs to investigate allostery across different protein families.
IFN protein engineering
Collaborating with an experimental group to help solve the first known structure of IFNλ4 using insights from molecular simulations.
COVID-19 therapeutics
Developed both novel and repurposed drugs for various proteins of the SARS-CoV-2 virus using computational tools.
Metamaterials Design
Designing networks to possess specific properties that are not normally found in nature. Key areas include:
Inverse design of auxetic and acoustic properties with automatic differentiation
Designing networks that are optimized for being auxetic and creates bandgaps upon compression.