A generalized and extensible machine-learned molecular mechanics force field trained on over 1.1 million QC data applicable for drug discovery applications. Figure reproduced from the arXiv:201001196 preprint under the arXiv non-exclusive license.
We present a multi-fidelity method for optimizing nonbonded force field parameters against physical property data. Leveraging fast surrogate models, we accelerate the parameter search and find novel solutions that improve force field performance.
The Open Force Field software stack is employed to automatically train a transferable, small molecule force field, based on the double exponential functional form, on over 1000 experimental condensed phase physical properties.
We report the results of the SAMPL9 host–guest blind challenge for predicting binding free energies.
We propose Grappa, a machine learned molecular mechanics force field for proteins. Grappa, operating on the molecular graph, accurately predicts energies and forces and agrees with experimental data such as J-couplings and folding free energies.