Data for: ART-SM: Boosting Fragment-Based Backmapping by Machine Learning

DOI

The simulation files, molecule topologies, and analysis workflows required to generate the results of our paper 'ART-SM: Boosting Fragment-Based Backmapping by Machine Learning' published in J. Chem. Theory Comput..

In details:

simulations.tar.gz: Contains the pdb (molecular structure), xtc (trajectory), mdp (MD parameters), itp (topology), and top (topology) files. GROMACS 2021 or 2023 was used for the simulations (see paper for details). Additionally, ART-SM mapping files (generated with version 1.0) and Backward mapping files from coarse-grained to atomistic resolution are included. workflows.tar.gz: Snakemake was used to handle the analysis workflows. The corresponding Snakefiles and python/bash scripts to reproduce the results in the paper are included. The actual results are not included (see paper instead). The main packages required for reproducing the results are listed under 'Software Metadata - Software Requirements'. If a second version is specified it was used only for 'sds_capb_section_4_4'. The first specified version was used for all other analyses.

Please have a look at the README files contained in each .tar.gz file.

ART-SM, 0.1 and 1.0

GROMACS, 2021 and 2023

Identifier
DOI https://doi.org/10.18419/DARUS-4134
Related Identifier IsSupplementTo https://doi.org/10.1021/acs.jctc.5c00189
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-4134
Provenance
Creator Pluhackova, Kristyna ORCID logo; Pfaendner, Christian (ORCID: 0009-0008-2953-669X); Unger, Benjamin ORCID logo; Korn, Viktoria Helena ORCID logo
Publisher DaRUS
Contributor Pluhackova, Kristyna
Publication Year 2025
Funding Reference DFG EXC 2075 - 390740016 ; Ministry of Science, Research and the Arts Baden-Württemberg Artificial Intelligence Software Academy (AISA)
Rights info:eu-repo/semantics/openAccess
OpenAccess true
Contact Pluhackova, Kristyna (University of Stuttgart)
Representation
Resource Type Dataset
Format application/gzip
Size 3763506501; 183091
Version 1.0
Discipline Chemistry; Natural Sciences; Physics