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

Eastman, Peter; Behara, Pavan Kumar; Dotson, David L.; Galvelis, Raimondas; Herr, John E.; Horton, Josh T.; Mao, Yuezhi; Chodera, John D.; Pritchard, Benjamin P.; Wang, Yuanqing; De Fabritiis, Gianni; Markland, Thomas E.

doi:http://dx.doi.org/10.1038/s41597-022-01882-6

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

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Eastman 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/s41597-022-01882-6

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.