Biomolecular Multiscale Simulation (BMS25) Dataset
Training Set for Neural Network Potentials for Electrostatic-Embedding QM/MM Settings
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Author / Producer
Date
2025-12-08
Publication Type
Dataset
ETH Bibliography
yes
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Rights / License
Abstract
Using an accelerated sampling approach, we generated the biomolecular multiscale simulation (BMS25) dataset with nearly 60'000 topologies and more than 1.5 million unique conformations of peptides and miniproteins as well as small molecules and transition states from chemical reactions. The dataset includes energies, gradients, and multipoles of solute molecules as well as gradients on solvent molecules at the wB97M-D4/ma-def2-TZVPP level of theory, enabling the development of multiscale neural network potentials for simulating large biomolecular systems.
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Publication status
External links
Editor
Contributors
Contact person: Riniker, Sereina
Data collector: Thürlemann, Moritz
Data collector : Pultar, Felix
Data collector : Gordiy, Igor
Project leader: Riniker, Sereina
Research group: Riniker, Sereina
Book title
Journal / series
Volume
Pages / Article No.
Publisher
ETH Zurich
Event
Edition / version
2025
Methods
Software
ORCA (5.0.4)
OpenMM (8.0)
Geographic location
Date collected
Date created
Subject
Computational chemistry; Quantum chemistry; Machine learning
Organisational unit
02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.
Notes
Funding
212732 - Combining Molecular Dynamics and Machine Learning for Free Energy Calculation with Quantum-Mechanical Accuracy (SNF)