Automated Molecular Simulations for High-throughput Drug Design

Students will build a comprehensive workflow that automates molecular dynamics simulations for drug discovery applications. The project involves simulating interactions between proteins and potential drug molecules, calculating interaction features from these simulations, and implementing machine learning models to rank design ideas by predicted binding affinity.

Students will work with real pharmaceutical datasets and validate their methods against experimental results. The goal is to create an accessible computational tool that can accelerate the early stages of drug development by identifying promising compounds through simulation rather than expensive laboratory experiments.