SPICE, a dataset of drug-like molecules and peptides for training machine learning potentials Review
| Authors: | Eastman, P.; Behara, P. K.; Dotson, D. L.; Galvelis, R.; Herr, J. E.; Horton, J. T.; Mao, Y.; Chodera, J. D.; Pritchard, B. P.; Wang, Y.; De Fabritiis, G.; Markland, T. E. |
| Review Title: | SPICE, a dataset of drug-like molecules and peptides for training machine learning potentials |
| Abstract: | Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe the SPICE dataset, a new quantum chemistry dataset for training potentials relevant to simulating drug-like small molecules interacting with proteins. It contains over 1.1 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 15 elements, charged and uncharged molecules, and a wide range of covalent and non-covalent interactions. It provides both forces and energies calculated at the ωB97M-D3(BJ)/def2-TZVPPD level of theory, along with other useful quantities such as multipole moments and bond orders. We train a set of machine learning potentials on it and demonstrate that they can achieve chemical accuracy across a broad region of chemical space. It can serve as a valuable resource for the creation of transferable, ready to use potential functions for use in molecular simulations. © 2022, The Author(s). |
| Keywords: | proteins; protein; peptide; chemistry; peptides; computer simulation; conformation; molecular conformation; machine learning |
| Journal Title: | Scientific Data |
| Volume: | 10 |
| ISSN: | 2052-4463 |
| Publisher: | Nature Publishing Group |
| Date Published: | 2023-01-04 |
| Start Page: | 11 |
| Language: | English |
| DOI: | 10.1038/s41597-022-01882-6 |
| PUBMED: | 36599873 |
| PROVIDER: | scopus |
| PMCID: | PMC9813265 |
| DOI/URL: | |
| Notes: | Data Paper -- Export Date: 1 February 2023 -- Source: Scopus |
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