Dataset Card for SurGBSA

Data used in the development of SurGBSA: Learning Representations From Molecular Dynamics Simulations.

Jones, D., Yang, Y., Lightstone, F. C., Moshiri, N., Allen, J. E., & Rosing, T. S. (2025). SurGBSA: Learning representations from molecular dynamics simulations. In arXiv [q-bio.BM]. arXiv. http://arxiv.org/abs/2509.03084

LLNL-DATA-2016815

Dataset Details

Atomistic Molecular Dynamics trajectories for the PDBBind CASF-2016 benchmark set with snapshot-resolved Molecular Mechanics Generalized Born Surface Area measurements.

Dataset Description

This dataset is derived from the PDBBind CASF-2016 benchmark, as a representative subsample of the refined set. 10ns MD simulations are provided for 242/285 structures in the benchmark using up to 6 starting points per structure. The crystal structure is included as a starting point for all structures and up to 5 Vina docking poses are provided as additional starting points. We include the raw Amber MD simulation files for the production runs, the preprocessed "ml-ready" files in numpy format, and mmgbsa scoring for each snapshot. We additionally provide the splits used for k-fold cross validation and the corresponding model weights.

• License: Creative Commons Attribution 4.0 (CC-BY-4.0)

Dataset Sources

• Repository: https://github.com/llnl/SurGBSA
• Paper: https://arxiv.org/abs/2509.03084

Uses

This resource is meant to be used for the development of machine learning algorithms and for applications in drug discovery research.

Source Data

The source data is taken from the PDBBind database.

Personal and Sensitive Information

This dataset does not contain personal, sensitive, or private information.

Citation

BibTeX:

@ARTICLE{Jones2025-dj, title = "{SurGBSA}: Learning representations from molecular dynamics simulations", author = "Jones, Derek and Yang, Yue and Lightstone, Felice C and Moshiri, Niema and Allen, Jonathan E and Rosing, Tajana S", journal = "arXiv [q-bio.BM]", month = sep, year = 2025, archivePrefix = "arXiv", primaryClass = "q-bio.BM" }

APA:

Jones, D., Yang, Y., Lightstone, F. C., Moshiri, N., Allen, J. E., & Rosing, T. S. (2025). SurGBSA: Learning representations from molecular dynamics simulations. In arXiv [q-bio.BM]. arXiv. http://arxiv.org/abs/2509.03084

Dataset Card Contact

Please contact jones289 [at] llnl.gov for questions related to this work.