SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials.
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials.
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DOI：
10.1038/s41597-022-01882-6
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发表时间：
2023-01-04
期刊：
影响因子：
9.8
通讯作者：
Markland, Thomas E.
中科院分区：
文献类型：
--
作者：
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.
机器学习潜力是分子模拟的重要工具，但它们的发展受到缺乏高质量数据集的阻碍。我们描述了SPICE数据集，这是一个新的量子化学数据集，用于训练与模拟药物样小分子与蛋白质相互作用相关的潜力。它包含超过110万种不同的小分子、二聚体、二肽和溶剂化氨基酸的构象。它包括15个元素，带电和不带电的分子，以及广泛的共价和非共价相互作用。它提供了在ω B 97 M-D3（BJ）/def 2-TZVPPD理论水平上计算的力和能量，沿着其他有用的量，如多极矩和键级。我们在其上训练了一组机器学习潜力，并证明它们可以在广泛的化学空间区域内实现化学准确性。它可以作为一个有价值的资源，用于创建可转移的，准备使用潜在的功能，用于分子模拟。
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.
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影响因子：
4.4
作者：
Gastegger, Michael;Kauffmann, Clemens;Marquetand, Philipp
通讯作者：
Marquetand, Philipp
影响因子：
9.8
作者：
Isert, Clemens;Atz, Kenneth;Jimenez-Luna, Jose;Schneider, Gisbert
通讯作者：
Schneider, Gisbert
影响因子：
4.4
作者：
Mardirossian, Narbe;Head-Gordon, Martin
通讯作者：
Head-Gordon, Martin
影响因子：
8.4
作者：
Lahey SJ;Rowley CN
通讯作者：
Rowley CN
DOI：
10.1039/d2dd00008c
发表时间：
2022-06-13
期刊：
DIGITAL DISCOVERY
影响因子：
--
作者：
Haghighatlari, Mojtaba;Li, Jie;Guan, Xingyi;Zhang, Oufan;Das, Akshaya;Stein, Christopher J.;Heidar-Zadeh, Farnaz;Liu, Meili;Head-Gordon, Martin;Bertels, Luke;Hao, Hongxia;Leven, Itai;Head-Gordon, Teresa
通讯作者：
Head-Gordon, Teresa