About the Speaker:

Bio:

David Mobley did his undergraduate work in Physics at the University of California, Davis, graduating in 2000. After that he finished a M.S. (2002) and a Ph.D. (2004) in Physics at UCD, doing graduate research in condensed matter theory and biophysics with Daniel L. Cox and Rajiv Singh. Following his Ph.D. 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 the University of California, Irvine. 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, then was promoted to Professor in 2018. He served on the advisory board for Schrodinger software from 2013-2016. He currently serves on the editorial boards of the Journal of Computer-Aided Molecular Design and the Journal of Molecular Recognition, on the scientific advisory board for OpenEye Scientific Software, as a founding and managing editor for the Living Journal of Computational Molecular Science, and as an Open Science Fellow for Silicon Therapeutics. He also maintains a personal blog where he writes about his Christian faith and other issues.

More on Prof. Mobley’s research and lab can be found here.

About the Seminar:

Title:

Improving binding free energy calculations to help guide pharmaceutical drug discovery

Abstract:

I will give an update on recent work in my group improving the tools used in the pharmaceutical industry to help guide pharmaceutical drug discovery. In particular, I’ll report on recent work in collaboration with Pfizer in which we came up with a more flexible approach to help guide early stage lead optimization using computational methods. Our approach, SepTop, appears comparable to or better than industry-leading methods in terms of affinity prediction, while also offering greater flexibility than typical methods. I’ll briefly discuss how our approach, called SepTop, works and then discuss test applications of this approach doing scaffold hopping on pharmaceutical targets (MALT1 and BACE, among others). I’ll also discuss remaining challenges/directions for future work.