David Mobley did his undergraduate work in Physics at the University of California, Davis, graduating in 2000. After that he finished a MS (2002) and a PhD (2004) in Physics at UCD, doing graduate research in condensed matter theory and biophysics with Daniel L. Cox and Rajiv Singh. Following his PhD he took a position with Ken Dill at the University of California, San Francisco, doing molecular simulations relating to protein-ligand binding and hydration thermodynamics. This work ultimately moved with him to the University of New Orleans. However, after his postdoc ended in 2008, he did a brief stint as Chief Science Officer of a startup company (Simprota Corporation) doing contract work designing peptide diagnostics. He then left to join UNO in Fall 2008. Then, in 2012, after almost 4 years at UNO, he moved to his current position in Pharmaceutical Sciences at UCI. In January 2013 he also received a joint appointment in the Department of Chemistry at UCI. He was promoted to Associate Professor at UCI, and received tenure, in July 2014. He served on the advisory board for Schrodinger software from 2013-2016. He currently serves on the editorial board of the Journal of Computer-Aided Molecular Design and on the scientific advisory board for OpenEye Scientific Software. He is an Open Science Fellow with pharmaceutical startup Silicon Therapeutics. He has received several honors and awards, including an NSF CAREER award, and the Hewlett-Packard Outstanding Junior Faculty Award from the American Chemical Society.
We develop, test, and apply computational tools to help guide pharmaceutical drug discovery. Early stage drug discovery has historically been a time-consuming process filled with trial and error; we want to change this so the process can be guided by accurate computational tools that predict binding affinity, specificity, selectivity, drug resistance, solubility and other factors. Our work focuses on making this happen, beginning from basic physical principles and pushing the frontiers of computer modeling using advanced simulation techniques and hardware.
Research projects in the group range from basic to applied. We are best known for our work improving computer methods for predicting binding of small molecules to proteins, and made key advances in this area that have helped push the techniques we use, “alchemical” free energy calculations, towards widespread use in the pharmaceutical industry. Currently, we continue to make these techniques faster, more robust, and more efficient, and bring them to bear on new areas. But we are also exploring collaborations and partnerships where we can apply our tools in active drug discovery efforts. In addition to our work on these techniques, we also help run a series of blind prediction challenges that are designed to drive innovations in software in the field in general.
3134B Natural Sciences 1
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