Nutmeg and SPICE: Models and data for biomolecular machine learning

Peter Eastman, Benjamin P. Pritchard, John D. Chodera, Thomas E. Markland
Journal of Chemical Theory and Computation 20:8583, 2024.
[DOI] [preprint]

We present a significant expansion of the SPICE dataset, a large-scale quantum chemical dataset for training machine learning potentials, and show how it can be used to build extremely accurate machine learning potentials.

Fitting quantum machine learning potentials to experimental free energy data: Predicting tautomer ratios in solution

Marcus Wieder, Josh Fass, and John D. Chodera
Chemical Science, in press [bioRxiv] [code]

We demonstrate, for the first time, how alchemical free energy calculations can performed on systems simulated entirely with quantum machine learning potentials and how these potentials can be retrained on experimental free energies to generalize to new molecules from limited training data. We apply this approach to a difficult problem in small molecule drug discovery: Predicting accurate tautomer ratios in solution.

Capturing non-local through-bond effects when fragmenting molecules for quantum chemical torsion scans

Chaya D. Stern, Christopher I. Bayly, Daniel G. A. Smith, Josh Fass, Lee-Ping Wang, David L. Mobley, and John D. Chodera
Preprint ahead of submission
[bioRxiv] [GitHub code] [GitHub data] [Metadata] [OSF]