SPICE, a dataset of drug-like molecules and peptides for training machine learning potentials

Eastman, PeterBehara, Pavan KumarDotson, David L.Galvelis, RaimondasHerr, John E.Horton, Josh T.Mao, YuezhiChodera, John D.Pritchard, Benjamin P.Wang, YuanqingDe Fabritiis, GianniMarkland, Thomas E.2023-02-212023-02-212023Eastman P, Behara PK, Dotson DL, Galvelis R, Herr JE, Horton JT, et al. SPICE, a dataset of drug-like molecules and peptides for training machine learning potentials. Sci Data. 2023 Jan 4;10(1):11. DOI: 10.1038/s41597-022-01882-62052-4463http://hdl.handle.net/10230/55833Machine 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.application/pdfeng© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.SPICE, a dataset of drug-like molecules and peptides for training machine learning potentialsinfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1038/s41597-022-01882-6Computational chemistryDensity functional theoryMolecular dynamicsProteinsinfo:eu-repo/semantics/openAccess