Greg Bowman

Greg Bowman is a Professor in the Department of Biochemistry and Biophysics at the University of Pennsylvania's School of Engineering and Applied Science. He leads the Bowman Lab, which focuses on understanding protein dynamics to advance therapeutic development and interpret genetic variation. His research integrates biophysical experiments, machine learning, physics-based simulations, and the Folding@home distributed computing project, one of the world’s largest volunteer computing systems.

His research interests lie at the intersection of computational biophysics and biomedical innovation. He develops and applies advanced methods such as Markov state models (MSMs), adaptive sampling algorithms, and deep learning to map the conformational landscapes of proteins. His work targets critical global health issues, including Alzheimer’s disease and emerging viral threats like SARS-CoV-2 and Ebola. A major focus is uncovering cryptic pockets and allosteric mechanisms to expand the druggable proteome, particularly for proteins considered 'undruggable' due to lack of traditional binding sites.

Although no articles are listed in the provided text, his research output centers on protein dynamics, simulation methodology, and structure-based drug design, with applications in neurodegeneration and virology. His lab has developed key open-source software tools including MSMBuilder, Enspara, PocketMiner, and FAST, which are widely used in the computational biophysics community.

Scientific honors include being named the Louis Heyman University Professor, a distinguished title at the University of Pennsylvania. His work is supported by large-scale collaborative science and public engagement through Folding@home, which involves over 200,000 citizen scientists worldwide.

He actively mentors a diverse team of postdoctoral scholars, graduate students, and undergraduate researchers. His mentoring philosophy emphasizes unlocking each trainee’s potential and promoting inclusivity in STEM, informed by his personal experience as a legally blind scientist. He encourages public engagement and supports outreach initiatives within his lab.

The Bowman Lab operates at the forefront of integrative biophysics, combining cutting-edge computational methods with experimental validation. The lab collaborates extensively and maintains a strong open-science ethos, sharing code via GitHub and involving the global community through Folding@home. Their adaptive sampling and deep learning approaches enable unprecedented insights into protein behavior and disease mechanisms.