About

David Mobley is a Professor in Pharmaceutical Sciences at the University of California, Irvine, with a background in Physics from the University of California, Davis, where he completed his undergraduate, M.S., and Ph.D. degrees. His graduate research focused on condensed matter theory and biophysics, working with Daniel L. Cox and Rajiv Singh. Following his Ph.D., he held a position at the University of California, San Francisco, working on molecular simulations related to protein-ligand binding and hydration thermodynamics, which he continued at the University of New Orleans. After a brief stint as Chief Science Officer of Simprota Corporation, he joined UNO in 2008 and later moved to UC Irvine in 2012, where he received tenure and was promoted to Professor in 2018. His research interests include computational chemistry, molecular modeling, and drug discovery, with active involvement in open science initiatives such as the Open Molecular Software Foundation, Open Force Field, and Open Free Energy projects. He serves on editorial boards and scientific advisory boards, and has contributed to advancing open technologies in molecular sciences.

Research topics

• Computer Science
• Physics
• Quantum mechanics
• Statistical physics
• Mathematics
• Chemistry
• Chemical physics
• Classical mechanics
• Physical chemistry
• Materials science
• Reliability engineering
• Engineering
• Statistics
• Algorithm
• Computational chemistry
• Telecommunications
• Thermodynamics
• Biology
• Environmental science
• Geometry
• Biochemistry
• Computational biology
• Cell biology

Selected publications

• Development and Benchmarking of Open Force Field v1.0.0—the Parsley Small-Molecule Force Field

  Journal of Chemical Theory and Computation · 2021 · 167 citations

  • Computer Science
  • Computer Science
  • Physics

  We present a methodology for defining and optimizing a general force field for classical molecular simulations, and we describe its use to derive the Open Force Field 1.0.0 small-molecule force field, codenamed Parsley. Rather than using traditional atom typing, our approach is built on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which handles increases in the diversity and specificity of the force field definition without needlessly increasing the complexity of the specification. Parameters are optimized with the ForceBalance tool, based on reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These quantum reference data are computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. In this initial application of the method, we present essentially a full optimization of all valence parameters and report tests of the resulting force field against compounds and data types outside the training set. These tests show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields, as is accuracy on binding free energies. We find that this initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems. Additionally, the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.

  DOI

• An optimized chemical-genetic method for cell-specific metabolic labeling of RNA

  Nature Methods · 2020 · 64 citations

  • Biology
  • Computational biology
  • Biochemistry
  DOI

• Best Practices for Alchemical Free Energy Calculations [Article v1.0].

  Living Journal of Computational Molecular Science · 2020 · 240 citations

  • Computer Science
  • Statistical physics
  • Computer Science

  Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing alchemical intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly. While these methods are highly flexible, care must be taken in avoiding common pitfalls to ensure that computed free energy differences can be robust and reproducible for the chosen force field, and that appropriate corrections are included to permit direct comparison with experimental data. In this paper, we review current best practices for several popular application domains of alchemical free energy calculations performed with equilibrium simulations, in particular relative and absolute small molecule binding free energy calculations to biomolecular targets.

  DOI

• The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

  Journal of Computer-Aided Molecular Design · 2020 · 137 citations

  • Computer Science
  • Reliability engineering
  • Environmental science

  Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

  DOI

• Non-bonded force field model with advanced restrained electrostatic potential charges (RESP2)

  Communications Chemistry · 2020 · 295 citations

  • Statistical physics
  • Materials science
  • Chemistry

  The restrained electrostatic potential (RESP) approach is a highly regarded and widely used method of assigning partial charges to molecules for simulations. RESP uses a quantum-mechanical method that yields fortuitous overpolarization and thereby accounts only approximately for self-polarization of molecules in the condensed phase. Here we present RESP2, a next generation of this approach, where the polarity of the charges is tuned by a parameter, δ, which scales the contributions from gas- and aqueous-phase calculations. When the complete non-bonded force field model, including Lennard-Jones parameters, is optimized to liquid properties, improved accuracy is achieved, even with this reduced set of five Lennard-Jones types. We argue that RESP2 with δ≈0.6 (60% aqueous, 40% gas-phase charges) is an accurate and robust method of generating partial charges, and that a small set of Lennard-Jones types is good starting point for a systematic re-optimization of this important non-bonded term.

  DOI

Recent grants

• NIH Grant R15GM09625701A1

  NIH · $268k · 2015

• Accelerating drug discovery via ML-guided iterative design and optimization

  NIH · $2.2M · 2023–2028

• CAREER: Computing accurate free energies for solubility, solvation, and transfer

  NSF · $601k · 2014–2020

• Computational alchemy for molecular design and optimization

  NIH · $2.2M · 2014–2024

• Advancing predictive physical modeling through focused development of model systems to drive new modeling innovations

  NIH · $1.2M · 2018–2023

Frequent coauthors

• Pavan Kumar Behara

  University of California, Irvine

  140 shared

• John D. Chodera

  130 shared

• Michael M. Henry

  Open Geospatial Consortium

  128 shared

• Chapin E. Cavender

  University of California, San Diego

  127 shared

• Alexander M. Payne

  Memorial Sloan Kettering Cancer Center

  123 shared

• Kenichiro Takaba

  Asahi Kasei (Germany)

  123 shared

• Christopher R. Iacovella

  Vanderbilt University

  122 shared

• Christopher I. Bayly

  89 shared

Labs

• Mobley LabPI

Education

• B.S., Physics

  University of California, Davis

  2000

• M.S., Physics

  University of California, Davis

  2002

• Ph.D., Physics

  University of California, Davis

  2004

Awards & honors

• Hewlett-Packard Outstanding Junior Faculty Award, American C…
• National Science Foundation CAREER Award, 2014

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