Nutmeg and SPICE: Models and data for biomolecular machine learning Journal Article
| Authors: | Eastman, P.; Pritchard, B. P.; Chodera, J. D.; Markland, T. E. |
| Article Title: | Nutmeg and SPICE: Models and data for biomolecular machine learning |
| Abstract: | We describe version 2 of the SPICE data set, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original data set by adding much more sampling of chemical space and more data on noncovalent interactions. We train a set of potential energy functions called Nutmeg on it. They are based on the TensorNet architecture. They use a novel mechanism to improve performance on charged and polar molecules, injecting precomputed partial charges into the model to provide a reference for the large-scale charge distribution. Evaluation of the new models shows that they do an excellent job of reproducing energy differences between conformations even on highly charged molecules or ones that are significantly larger than the molecules in the training set. They also produce stable molecular dynamics trajectories and are fast enough to be useful for routine simulation of small molecules. |
| Keywords: | accuracy; water; parameters |
| Journal Title: | Journal of Chemical Theory and Computation |
| Volume: | 20 |
| Issue: | 19 |
| ISSN: | 1549-9618 |
| Publisher: | American Chemical Society |
| Date Published: | 2024-10-08 |
| Start Page: | 8583 |
| End Page: | 8593 |
| Language: | English |
| ACCESSION: | WOS:001320392200001 |
| DOI: | 10.1021/acs.jctc.4c00794 |
| PROVIDER: | wos |
| PUBMED: | 39318326 |
| PMCID: | PMC11753618 |
| Notes: | Article -- MSK Cancer Center Support Grant (P30 CA008748) acknowledged in PDF -- Source: Wos |
