Roivant Discovery Announces 2021 Open Science Fellows

Drs. Gregory Bowman, Alex Dickson and Paul Robustelli selected as fellows due to shared focus on understanding the structures and dynamics of biological systems at atomic resolution.

NEW YORK and BOSTON, Oct. 29, 2021  Roivant Discovery, the drug discovery engine for Roivant Sciences (Nasdaq: ROIV), today announced that Dr. Gregory R. Bowman, Dr. Alex Dickson and Dr. Paul Robustelli have been appointed as 2021 Open Science Fellows. The Roivant Discovery Open Science Fellows program integrates cutting-edge research from leading academicians with the advanced computational physics research at Roivant Discovery. 

These three researchers were chosen due to their efforts to advance the understanding of structures and dynamics of biological systems at atomic resolution. Their research interests all relate to unraveling the molecular mechanisms associated with human diseases. Specific research areas include long-timescale simulations using weighted ensemble algorithms, the integration of biophysical data with molecular dynamics, and the use of nuclear magnetic resonance (NMR) spectroscopy to model the conformational ensembles of intrinsically disordered proteins at atomic resolution. 

“Open science is critical to our culture at Roivant Discovery. The 2021 Open Science Fellows were carefully selected from academic researchers with established leadership in their field who promote the open science ethos,” said Woody Sherman, Ph.D., chief computational scientist, Roivant Sciences. “We are proud to be working with these world-class scientists and look forward to advancing the field of predictive sciences in drug discovery.”

About the 2021 Roivant Discovery Open Science Fellows

Gregory R. Bowman, Ph.D. is an associate professor of biochemistry and molecular biophysics at Washington University in St. Louis, where his research group develops new ways to interpret genetic variation and combat global health threats by understanding and exploiting protein dynamics using a combination of biophysical experiments, machine learning, physics-based simulations, and the world’s largest distributed computer. Dr. Bowman’s group is particularly interested in developing methods to map out the ensemble of structures a protein adopts, identify allostery and cryptic pockets, and use this information to design new drugs and proteins.

Dr. Bowman’s honors and awards include a Packard Fellowship in Science and Engineering, a National Science CAREER Award, a Burroughs Wellcome Fund Career Award at the Scientific Interface (CASI) and a Miller Research Fellowship. He earned his B.S. in computer science from Cornell University and his Ph.D. in biophysics from Stanford University. He was a Miller Research Fellow at the University of California, Berkeley.

Alex Dickson, Ph.D. is an associate professor of biochemistry and molecular biology at Michigan State University. Dr. Dickson’s research group specializes in the development of new algorithms and software for molecular dynamics (MD) simulation. MD can reveal atomic-level insight into drug-receptor interactions, but conventional simulations are often too short to observe key events, such as the binding and unbinding pathways of drug molecules. Algorithms developed in the Dickson lab can generate binding and unbinding trajectories that typically occur on timescales thousands to millions of times longer than conventional MD simulations. This unlocks new insights into binding poses, binding mechanisms and binding kinetics that can be used to design potent and specific drug molecules.

Dr. Dickson has been recognized with the OpenEye Outstanding Junior Faculty Award from the American Chemical Society, as well as the Elizabeth R. Norton Prize for Excellence in Research in Chemistry from the University of Chicago. Research in his laboratory is funded by awards from the National Institutes of Health (NIH), as well as the Joint DMS/NIGMS Initiative to Support Research at the Interface of the Biological and Mathematical Sciences by the Division of Mathematical Sciences and the National Institute of General Medical Sciences (NIGMS) at the NIH and by the Human Frontiers Science Program. Dr. Dickson earned his B.S. in chemical physics from the University of Toronto and his M.S. and Ph.D. in chemistry from the University of Chicago. He was a postdoctoral fellow at the University of Michigan.

Paul Robustelli, Ph.D. is an assistant professor of chemistry at Dartmouth College, where his research focuses on the integration of computational and experimental methods to study dynamic and disordered proteins. Dr. Robustelli utilizes computer simulations and nuclear magnetic resonance (NMR) spectroscopy to model the conformational ensembles of intrinsically disordered proteins at atomic resolution to understand how small molecule drugs bind and inhibit disordered proteins and rationally design novel disordered protein inhibitors. Dr. Robustelli has made contributions to the development of physical models (“force fields”) that enable accurate simulations of disordered proteins and computational methods to integrate NMR data as restraints in molecular simulations.

Dr. Robustelli’s honors and awards include a National Science Foundation (NSF) Postdoctoral Research Fellowship, an NSF Graduate Research Fellowship and a Gates Cambridge Scholarship. Dr. Robustelli earned his B.A. in chemistry from Pomona College and his Ph.D. in chemistry from the University of Cambridge. He was also a postdoctoral fellow at Columbia University and a scientist at D.E. Shaw Research in New York.

About Roivant Discovery

Roivant Discovery, the drug discovery engine for Roivant Sciences, is focused on discovering transformative medicines. We believe the future of drug discovery hinges on the integration of leading-edge predictive science with excellence in experimental approaches. Roivant Discovery is pioneering a physics-driven approach to drug design that is tightly coupled with chemistry and biology research and development. Our industry-leading computational platform is purpose-built to develop novel small molecules and induced proximity modulators to address biologically and genetically validated, but previously intractable, protein targets through the combination of preeminent physics-based simulation tools with machine learning. The tight integration of our computational platform with our broad experimental capabilities and deep drug discovery experience enables the rapid design and optimization of new drugs to address a wide range of targets for diseases with high unmet need. For more information, please visit and follow us on LinkedInTwitter and YouTube.


Paul Davis