The Brown Lab at Virginia Tech is an interdisciplinary research group specializing in computational biochemistry, molecular modeling, and AI-enabled computer-aided drug discovery. We develop and apply computational methods, MD simulations, and machine learning approaches to investigate protein structure–function relationships, conformational dynamics, protein aggregation, and molecular interactions. Our research focuses on understanding the mechanisms governing amyloid assembly, protein–membrane interactions, and the influence of complex biological environments on protein behavior, with applications in neurodegenerative and metabolic diseases. We also advance structure-based drug discovery through the development of predictive methods for identifying and optimizing small molecules and peptides, with particular emphasis on antiviral drug discovery, antimicrobial resistance, kinases, and challenging protein–protein interactions.
Collaboration is central to our research. We work with experimental scientists at Virginia Tech and institutions worldwide, integrating computational approaches with experimental studies to address complex biological questions at the atomistic level.
Our commitment to undergraduate research and scientific education extends beyond the lab. We lead Virginia Tech's Discovery Lab, an interdisciplinary undergraduate research environment built around our published pedagogical framework for integrating authentic research experiences, peer mentorship, and experiential learning into undergraduate education. Through this model, we expand access to meaningful research opportunities while developing students' scientific independence, computational skills, and research competencies. Our broader educational efforts advance data science education, open and reproducible science, interdisciplinary training, and STEM workforce development, preparing the next generation of scientists to work across traditional disciplinary boundaries.
For research collaborations or inquiries, please contact Dr. Anne M. Brown via the Contact page.
