SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
This provides a valuable resource for researchers in computational chemistry and drug discovery, though it is incremental as it builds on existing dataset efforts.
The authors tackled the shortage of high-quality datasets for training machine learning potentials in molecular simulation by introducing the SPICE dataset, which contains over 1.1 million conformations for drug-like molecules and peptides, and they demonstrated that trained potentials achieve chemical accuracy across a broad chemical space.
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.