Supplementary Data for the Paper 'Moltiverse: Molecular Conformer Generation Using Enhanced Sampling Methods'
Creators: name: Bedoya, Mauricio
name: Adasme Carreño, Francisco
name: Peña-Martínez, Paula Andrea
name: Muñoz-Gutiérrez, Camila
name: Peña-Tejo, Luciano
name: Márquez-Montesinos, José C. E.
name: Hernández-Rodríguez, Erix W.
name: González, Wendy
name: Martinez, Leandro
name: Alzate-Morales, Jans
Publisher: name: figshare
Year: 2025
Subjects: classificationCode: 340701
subject: Computational chemistry
classificationCode: 310201
subject: Bioinformatic methods development

description: The Moltiverse code is hosted on GitHub at: https://github.com/ucm-lbqc/moltiverseDataset Structure:1. Moltiverse benchmark (01_moltiverse_conformers/). This folder contains raw data for all the generated conformers using Moltiverse for the Platinum Diverse Dataset and the Prime dataset.2. Analysis (02_analysis). Contains raw data, analysis notebooks, and statistical results.<br>Raw data files: rmsd_min, rmsd_min_low, rmsd_min_high, rmsd_matrix, rmsd_std, rmsd-rdgyt_corrAnalysis notebooks: ipynb notebooks<br>statistics_r/: Processed data and statistical analysis subfolders<br>3. Time performance (time_performance). This folder contains tools and results for performance evaluation.Analysis WorkflowStep 1: Data Export and Initial AnalysisA1_export_data_to_statistics_r.ipynbA2_plots_data_and_analysis.ipynbStep 2: Statistical ProcessingRun R Markdown scripts (.Rmd) in sequence from folders 01 through 06 in statistics_r/Step 3: Visualization and PlottingB1_plots_statistics_matrix.ipynbB2_plots_lineal.ipynbStep 4: Statistics by Replica AnalysisRun C1_plots_statistics_matrix_per_replica.ipynb in the 06_statistics_by_replica/ folder.<i>Notes: All paths are relative to the repository root. </i><i>Execute notebooks and scripts in the specified order to ensure proper data flow. </i><i>Ensure all dependencies are installed before running the analysis pipeline.</i>Attribution and License InformationThis project includes results processed from:"High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment" Thomas Seidel, Christian Permann, Oliver Wieder, Stefan M. Kohlbacher, and Thierry Langer; Journal of Chemical Information and Modeling. 2023 63 (17), 5549-5570DOI: 10.1021/acs.jcim.3c00563<br>SUPPLEMENTARY DATA - DOI: 10.5281/zenodo.8137603Original Authors: Seidel, Thomas, Permann, Christian, Wieder, Oliver, Kohlbacher, Stefan Michael, Langer, ThierryOriginal Release Date: 2023License InformationOriginal License: GPLv2Current License: GPLv2Date of License Update: 2024Proper CitationIf you use these scripts, please cite:"High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment" Thomas Seidel, Christian Permann, Oliver Wieder, Stefan M. Kohlbacher, and Thierry Langer; Journal of Chemical Information and Modeling. 2023 63 (17), 5549-5570DOI: 10.1021/acs.jcim.3c00563<br>Seidel, T., Permann, C., Wieder, O., Kohlbacher, S. M., &amp; Langer, T. (2023). Supplementary Data for the Paper 'High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment'. https://doi.org/10.5281/zenodo.8137603<br>Also cite this current repository and Moltiverse work (Check citation section at GitHub): https://github.com/ucm-lbqc/moltiverse