Toward learned chemical perception of force field typing rules

Camila Zanette, Caitlin C. Bannan, Christopher I. Bayly, Josh Fass, Michael K. Gilson, Michael R. Shirts, John Chodera, and David L. Mobley
Journal of Chemical Theory and Computation, 15:402, 2019. [DOI] [ChemRxiv] [GitHub]

We show how machine learning can learn typing rules for molecular mechanics force fields within a Bayesian statistical framework.

Overview of the SAMPL6 host-guest binding affinity prediction challenge

Andrea RizziSteven MurkliJohn N. McNeillWei YaoMatthew SullivanMichael K. Gilson, Michael W. Chiu, Lyle IsaacsBruce C. GibbDavid L. Mobley*, John D. Chodera*
* denotes co-corresponding authors
Journal of Computer-Aided Molecular Design special issue on SAMPL6, 32:937, 2018. [DOI] [bioRxiv] [GitHub]

We present an overview of the host-guest systems and participant performance for the SAMPL6 host-guest blind affinity prediction challenges, assessing how well various physical modeling approaches were able to predict ligand binding affinities for simple ligand recognition problems where receptor sampling and protonation state effects are eliminated due to the simplicity of supramolecular hosts. We find that progress is now stagnated likely due to force field limitations.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

Samuel Gill, Nathan M. Lim, Patrick Grinaway, Ariën S. Rustenburg, Josh Fass, Gregory Ross, John D. Chodera, and David Mobley.
Journal of Physical Chemistry B 22:5579, 2018. [DOI] [ChemRxiv] [GitHub]

Nonequilibrium candidate Monte Carlo can be used to accelerate the sampling of ligand binding modes by orders of magnitude over instantaneous Monte Carlo.

Approaches for calculating solvation free energies and enthalpies demonstrated with an update of the FreeSolv database

Guilherme Duarte Ramos Matos, Daisy Y. Kyu, Hannes H. Loeffler, John D. Chodera, Michael R. Shirts, David Mobley
Journal of Chemical Engineering Data 62:1559, 2017. [DOI] [bioRxiv] [GitHub]

We review alchemical methods for computing solvation free energies and present an update (version 0.5) to the FreeSolv database of experimental and calculated hydration free energies of neutral compounds.

Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge

Ariën S. Rustenburg, Justin Dancer, Baiwei Lin, Jianweng A. Feng, Daniel F. Ortwine, David L. Mobley, and John D. Chodera.
Journal of Computer-Aided Molecular Design 30:945, 2016. [DOI] [bioRxiv] [PDF] // data: [GitHub]
Solicited manuscript for special issue of the Journal of Computer Aided Molecular Design on the SAMPL5 Challenge.

The SAMPL Challenges have driven predictive physical modeling for ligand:protein binding forward by focusing the community on a series of blind challenges that evaluate performance on blind datasets, focus attention on current challenges for physical modeling techniques, and provide high-quality experimental datasets to the community after the challenge is over. For many years, challenges focused around hydration free energies have proven to be extremely useful, with theory now able to determine when experiment is wrong. To replace these challenges, since no more hydration free energy data is being measured, we proposed to use the partition or distribution coefficients of small druglike molecules between aqueous and apolar phases. We report the collection of cyclohexane-water partition data for a set of compounds used to drive the SAMPL5 distribution coefficient challenge, providing the experimental data, methodology, and insight for future iterations of this challenge.