QCell: Comprehensive Quantum-Mechanical Dataset Spanning Diverse Biomolecular Fragments
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| Format: | Preprint |
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2025
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| _version_ | 1866910008151638016 |
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| author | Kabylda, Adil Suárez-Dou, Sergio Davoine, Nils Brünig, Florian N. Tkatchenko, Alexandre |
| author_facet | Kabylda, Adil Suárez-Dou, Sergio Davoine, Nils Brünig, Florian N. Tkatchenko, Alexandre |
| contents | Recent advances in machine learning force fields (MLFFs) are revolutionizing molecular simulations by bridging the gap between quantum-mechanical (QM) accuracy and the computational efficiency of mechanistic potentials. However, the development of reliable MLFFs for biomolecular systems remains constrained by the scarcity of high-quality, chemically diverse QM datasets that span all of the major classes of biomolecules expressed in living cells. Crucially, such a comprehensive dataset must be computed using non-empirical or minimally empirical approximations to solving the Schrödinger equation. To address these limitations, we introduce the QCell dataset -- a curated collection of 525k new QM calculations for biomolecular fragments encompassing carbohydrates, nucleic acids, lipids, dimers, and ion clusters. QCell complements existing datasets, bringing the total number of available data points to 41 million molecular systems, all calculated using hybrid density functional theory with nonlocal many-body dispersion interactions, as captured by the PBE0+MBD(-NL) level of quantum mechanics. The QCell dataset therefore provides a valuable resource for training next-generation MLFFs capable of modeling the intricate interactions that govern biomolecular dynamics beyond small molecules and proteins. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_09939 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QCell: Comprehensive Quantum-Mechanical Dataset Spanning Diverse Biomolecular Fragments Kabylda, Adil Suárez-Dou, Sergio Davoine, Nils Brünig, Florian N. Tkatchenko, Alexandre Chemical Physics Recent advances in machine learning force fields (MLFFs) are revolutionizing molecular simulations by bridging the gap between quantum-mechanical (QM) accuracy and the computational efficiency of mechanistic potentials. However, the development of reliable MLFFs for biomolecular systems remains constrained by the scarcity of high-quality, chemically diverse QM datasets that span all of the major classes of biomolecules expressed in living cells. Crucially, such a comprehensive dataset must be computed using non-empirical or minimally empirical approximations to solving the Schrödinger equation. To address these limitations, we introduce the QCell dataset -- a curated collection of 525k new QM calculations for biomolecular fragments encompassing carbohydrates, nucleic acids, lipids, dimers, and ion clusters. QCell complements existing datasets, bringing the total number of available data points to 41 million molecular systems, all calculated using hybrid density functional theory with nonlocal many-body dispersion interactions, as captured by the PBE0+MBD(-NL) level of quantum mechanics. The QCell dataset therefore provides a valuable resource for training next-generation MLFFs capable of modeling the intricate interactions that govern biomolecular dynamics beyond small molecules and proteins. |
| title | QCell: Comprehensive Quantum-Mechanical Dataset Spanning Diverse Biomolecular Fragments |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2510.09939 |