Undergraduate Research: Computational Drug Discovery
- Participated in a research program on computational drug discovery
- Collaborated with a professor and research team to apply deep learning models for analyzing 3D images of protein-ligand interactions
- Implemented deep learning models using PyTorch to analyze 60,000+ protein-ligand interactions, improving binding affinity of generated molecules by 17% and synthesizability by 38% compared to prior models
- Accelerated drug discovery process by reducing analysis time through efficient model optimization
- Designed visualizations of generated molecules using Matplotlib to perform data analysis
- Prepared research paper detailing findings for submission to NeurIPS (In Progress)
- Developed practical skills in interdisciplinary teamwork, problem-solving, and the application of theoretical machine learning concepts in a real-world research setting.
