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353 results for “Molecular modeling”

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zenodo48/100

Dataset for "Large Language Models as molecular design engines"

<ol> <li>claude-gpt-paper.zip : This dataset contains data and results associated with the paper "Large Language Models as molecular design engines" The paper investigates the use of large language models, specifically Claude 3 Opus, for generating and analyzing chemical structures based on various prompts from A-H (as mentioned in the manuscript), and guided design related to electron-withdrawing groups (EWG), electron-donating groups (EDG).</li> </ol> The dataset includes: <ol> <li>PM7 MOPAC energy calculations for generated molecules, along with their SMILES representations and molecule IDs.</li> <li>PM7-calculated charges for the generated molecules.</li> <li>Output files from the Claude 3 Opus language model for each prompt category along.</li> <li>Original dataset (subset of ZINC database) used to build common keys and the initial design space.</li> <li>JSON file containing common keys for featurizing unknown SMILES.</li> <li>PCA object to convert molecule embeddings to 3-dimensional embeddings.</li> </ol> The data is organized into the following folders: <ul> <li><code>pm7_charge_results</code>: Contains HOMO-LUMO energy differences for plotting.</li> <li><code>pm7_charge_calculation</code>: Contains PM7 MOPAC energy calculations and charges.</li> <li><code>out</code>: Contains output files from the Claude 3 Opus language model.</li> <li><code>fact-dropbox</code>: Contains the original dataset, common keys, and PCA object file.</li> </ul> The data can be used to reproduce the results presented in the paper and serve as a foundation for further research in this area. For a detailed description of the folder structure and contents, please refer to the File_descriptions.md file included in the dataset. 2. llm-visulizer-dashapp.zip This is the code for the visualizer app for viewing the molecules generated by the LLM. The README.md file has details about running the app. 3. claude-gpt-paper-codes.zip  This contains the notebook GPT_modification_just_plots.ipynb for plotting, and other codes. The README.md file has details about running the main notebook for getting the plots.

opencc-by-4.0May 2024View details →

zenodo48/100

Dataset for Machine Learning Framework for Modeling Exciton-Polaritons in Molecular Materials

The data consists of several NumPy arrays saved in the binary format (npy files) with a total size of 108 MB. The details of these files are listed below. <table> <tbody> <tr> <td> Filename and path </td> <td> Description </td> </tr> <tr> <td> training/azo_R.npy </td> <td> Coordinates for training </td> </tr> <tr> <td> training/azo_Z.npy </td> <td> Atomic indices for training </td> </tr> <tr> <td> training/azo_E.npy </td> <td> Molecular energies for training </td> </tr> <tr> <td> training/azo_D.npy </td> <td> Transition dipoles for training </td> </tr> <tr> <td> training/azo_ScaledNACR.npy </td> <td> Non-adiabatic coupling vectors scaled by energy difference for training </td> </tr> <tr> <td> scan/azo_R.npy </td> <td> Coordinates for PES scan </td> </tr> <tr> <td> scan/azo_Z.npy </td> <td> Atomic indices for PES scan </td> </tr> <tr> <td> scan/azo_E.npy </td> <td> Molecular energies for PES scan </td> </tr> <tr> <td> scan/azo_D.npy </td> <td> Transition dipoles for PES scan </td> </tr> <tr> <td> scan/azo_ScaledNACR.npy </td> <td> Non-adiabatic coupling vectors scaled by energy difference for PES scan </td> </tr> <tr> <td> spectrum/azo_R.npy </td> <td> Coordinates for spectrum calculations </td> </tr> <tr> <td> spectrum/azo_Z.npy </td> <td> Atomic indices for spectrum calculations </td> </tr> <tr> <td> spectrum/azo_E.npy </td> <td> Molecular energies for spectrum calculations </td> </tr> <tr> <td> spectrum/azo_D.npy </td> <td> Transition dipoles for spectrum calculation </td> </tr> </tbody> </table>

opencc-by-4.0Sep 2023View details →

zenodo44/100

Logical model for Molecular Pathways Enabling Tumour Cell Invasion and Migration

Understanding the etiology of metastasis is very important in clinical perspective, since it is estimated that metastasis accounts for 90% of cancer patient mortality. Metastasis results from a sequence of multiple steps including invasion and migration. The early stages of metastasis are tightly controlled in normal cells and can be drastically affected by malignant mutations; therefore, they might constitute the principal determinants of the overall metastatic rate even if the later stages take long to occur. To elucidate the role of individual mutations or their combinations affecting the metastatic development, a logical model has been constructed that recapitulates published experimental results of known gene perturbations on local invasion and migration processes, and predict the effect of not yet experimentally assessed mutations. The model has been validated using experimental data on transcriptome dynamics following TGF-β-dependent induction of Epithelial to Mesenchymal Transition in lung cancer cell lines. A method to associate gene expression profiles with different stable state solutions of the logical model has been developed for that purpose. In addition, we have systematically predicted alleviating (masking) and synergistic pairwise genetic interactions between the genes composing the model with respect to the probability of acquiring the metastatic phenotype. We focused on several unexpected synergistic genetic interactions leading to theoretically very high metastasis probability. Among them, the synergistic combination of Notch overexpression and p53 deletion shows one of the strongest effects, which is in agreement with a recent published experiment in a mouse model of gut cancer. The mathematical model can recapitulate experimental mutations in both cell line and mouse models. Furthermore, the model predicts new gene perturbations that affect the early steps of metastasis underlying potential intervention points for innovative therapeutic strategies in oncology.   Included files: <ul> <li>Master Model: the model includes detailed regulation of the major players involved in the crosstalks between Notch and p53 pathways</li> <li>Modular Model: the model is a reduction of the master model. To reduce the master model, we lumped together some entities that belonged to a module.</li> </ul>

opencc-by-4.0Nov 2015View details →

zenodo44/100

Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site

Metabolomics dataset used in the publication "Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site" Jaanika Kronberg, Jonathan J. Byrne, Jeroen Jansen, Philipp Antczak, Adam Hines, John Bignell, Ioanna Katsiadaki, Mark R. Viant and Francesco Falciani  Metabolomics dataset for metabolic bins 1 to 1045 for 376 mussels as used in the publication. Mussel metadata are described in a separate file (spectrum number, sample label, sex, site, species, month, temperature of water, salinity of water, ADG rate, gonadal stage, parasite load) Species 1: Mytilus edulis, species 2: hybrid, species 3: Mytilus galloprovincialis

opencc-by-4.0Jan 2021View details →

zenodo44/100

Molecular Models and Wave Function Definitions for Models A-G of the [2Fe]F Cluster in FeFe-hydrogenase Maturase Enzyme HydF

The dataset contains all relevant atomic positional coordinates for 2Fe-cluster models, and electronic wave function data (using formatted Gaussian checkpoint files) as described in the related publication (see citation below). The version 2.0 contains additional models for [2Fe-2S] cluster linked [2Fe]F constructs. The top folder contains "analysis.xlsx" electronic spreadsheet that summarizes all the numerical results for absolute and relative electronic energy values, internal coordinates, calculated and scaled vibrational frequencies for diatomic stretching modes. The details of developing scaled quantum forcefields as a function of level of theory and model composition are also given. The schematic structural definitions are given in the "models.pdf" file and keys for abbreviations are provided in "symbols.txt" file.

opencc-by-4.0Nov 2017View details →

zenodo44/100

Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling: Datasets.

Datasets related to the publication [1]. Including: <ul> <li>KRAS G12X mutations derived from COSMIC v.79 [http://cancer.sanger.ac.uk/cosmic/] (KRAS_G12X_mut_COSMICv79..xlsx)</li> <li>RMSFs (300-2000ns) of GDP-systems (300_2000rmsf_GDP_systems_RAW_AVG_SE.xlsx)</li> <li>RMSFs (300-2000ns) of GTP-systems (300_2000RMSF_GTP_systems_RAW_AVG_SE.xlsx)</li> <li>PyInteraph analysis data for salt-bridges and hydrophobic clusters (.dat files for each system in the PyInteraph_data.zip-file)</li> <li>Backbone trajectories for each system (residues 4-164; frames for every 1ns). Last number (e.g. _1) refers to the replica of the simulated system.</li> <li>backbone_4-164.gro/.pdb/.tpr -files (resid 4-164)  </li> </ul> [1] Pantsar T et al. Assessment of mutation probabilities of KRAS G12 missense mutants and their long-time scale dynamics by atomistic molecular simulations and Markov state modeling. PLoS Comput Biol Submitted (2018)

opencc-by-4.0Aug 2018View details →

zenodo44/100

The molecular architecture of the yeast spindle pole body core determined by Bayesian integrative modeling

This repository pertains to the molecular architecture of the yeast spindle pole body (SPB), the structural and functional equivalent of the metazoan centrosome. Data from in vivo FRET and yeast two-hybrid, along with SAXS, X-ray crystallography, and electron microscopy were integrated by a Bayesian structure modeling approach. For more information about how to reproduce this modeling, see the <a href="https://salilab.org/spb/">Sali lab website</a> or the README file.

opencc-by-sa-4.0Aug 2017View details →

zenodo44/100

Homology modelling, molecular docking and molecular dynamics simulations of wild type and mutant human CYP2J2 with three polyunsaturated fatty acids

This is the "parent" repository for the Data Note : "Molecular dynamics simulations of the interaction of wild type and mutant human CYP2J2 with polyunsaturated fatty acids" by Abelak, Bishop-Bailey and Nobeli. It contains a document (Abelak_etal_Methods.pdf) describing the methods used to produce the data here and the data in all repositories supplementing it. It also contains a shell script (create_sim4_repeats.sh) that is typical of those used to set up the molecular dynamics simulations in the repositories supplementing this one. Finally, it contains the results of the homology modelling and docking simulations that formed the starting points for the molecular dynamics simulations in this study. Description of files in this dataset: C2J2_min3_mod_noH.pdb : Homology model of the wild type CYP2J2 built from an alignment of templates with PDB ids: 1SUO, 2P85, 3EBS and 1Z10. docking_wild_type_C2J2.zip : Nine docked poses of arachidonic acid docked to the homology model of the wild type CYP2J2. Details of how this data was produced is available in the Abelak_etal_Methods.docx document.

opencc-by-4.0Sep 2019View details →

Figshare40/100

Screen captures illustrating molecular 3D model sharing through Sketchfab, Google Poly and NIH Print Exchange

Sharing 3D models illustrated by 6 screen captures.    1: cardboard stereo view with Sketchfab of example 1 (ACE-spike coronavirus complex)   2: tuning of VR/AR settings on the Sketchfab platform (example 1)   3: Sketchfab web view of example 1   4: Sketchfab 3D Model inspector applied to example 1 model   4: Google Poly web view of example 1

opencc-by-4.0Dec 2019View details →

zenodo40/100

Data for the article: "Molecular Modelling Reveals Eight Novel Druggable Binding Sites in SARS-CoV-2's Spike Protein" by Ilke Ugur and Antoine Marion

This upload contains data related to the article published as a preprint on ChemRxiv with DOI https://doi.org/10.26434/chemrxiv.13292768 "Molecular Modelling Reveals Eight Novel Druggable Binding Sites in SARS-CoV-2's Spike Protein" by Ilke Ugur and Antoine Marion (2020) Department of Chemistry, Middle East Technical University, Ankara, Turkey. For further information, please contact: ilkeugur@metu.edu.tr ; amarion@metu.edu.tr The manuscript is currently under peer-review. Content: Library of molecules derived from DrugBank v 5.1.5: - DrugBank_2020_5.1.5/        # All necessary files for the docking and refinement of the library of molecules. -- DB_5.1.5_pH7.4_pdbqt/      ## PDBQT readily usable for docking with AutoDock Vina. -- DB_5.1.5_pH7.4_mol2amber/  ## mol2 files containing assigned GAFF atom types and Gasteiger atomic charges. -- DB_5.1.5_pH7.4_frcmod/     ## frcmod files containing missing molecular mechanics parameters -- dbID_name.dat              ## DrugBank ID to generic name dictionary Note: The files were prepared automatically via a series of operations handling openbabel and antechamber.       The protonation state of ionizable groups as well as Gasteiger atomic charges were assigned by openbabel for a pH of 7.4       mol2 and frcmod files can be used readily via the tleap module of AmberTools to produce topology files. Receptor structures: - receptors/                  # PDB files for the four structures of the spike protein considered in this work -- CS00ns.pdb                 ## Closed state after the remodelling of missing loops (PDB ID 6vxx) -- OS00ns.pdb                 ## Open   state after the remodelling of missing loops (PDB ID 6vyb) -- CS25ns.pdb                 ## Closed state after 25 ns of molecular dynamics in explicit water -- OS25ns.pdb                 ## Open   state after 25 ns of molecular dynamics in explicit water Note: All structures are aligned to CS00ns.pdb and can be converted to pdbqt for docking with AutoDock Vina Docking grid centers: - dockingCenters/             # XYZ files containing the coordinates of each docking grid center considered in this work Note: The coordinates are given in the same frame as that of the four structures of the receptor. Binding sites: - bindingSites/               # XYZ files with the coordinates of the representative atomic centres                               # of each binding site identified in this work (A-H). Note: These files can be used to get a clearer picture of the binding sites within the structures       of the spike protein shared in the receptors directory. Final modelling results: - allData.txt                 # data for all molecules in the set (approved and investigational) - appData.txt                 # data for approved molecules only - data.xlsx                   # data for all molecules in the set (approved and investigational)                               # as a formatted excel spreadsheet Note: The columns are delimited with semi-colons ";".       The files contain the results for the best pose of all approved molecules for which       molecular mechanics-based geometry optimization succeeded, regardless of their score.       For other molecules, the result of their best pose is reported only for those complexes       having MM interaction energy lower or equal to -22.00 kcal/mol. Visualization: - bs.pse                      # pymol session representing the binding sites within the                               # closed state structure of the spike protein (CS00ns) - pt.pse                      # pymol session representing the docking grid centres within                               # closed statestructure of the spike protein (CS00ns) Note: the PSE files should be compatible with version 7.0 of pymol and later

opencc-by-4.0Dec 2020View details →

zenodo40/100

Associated code and data for "Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization (doi: 10.3389/fcell.2021.74635)"

This deposit contains the data, code, and analysis to reproduce the results in the manuscript - Lai X, Keller C, Santos-Rosales G, Schaft N, Dörrie J, Vera J. Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization. Frontiers in Cell and Developmental Biolology. 2022; 9:746359; <a href="https://www.researchgate.net/publication/358461035_Multi-Level_Computational_Modeling_of_Anti-Cancer_Dendritic_Cell_Vaccination_Utilized_to_Select_Molecular_Targets_for_Therapy_Optimization">doi:10.3389/fcell.2021.746359</a>. If you have used the code for your research, please cite the original publication. Thank you very much.

opencc-by-4.0Nov 2021View details →

zenodo40/100

Dataset for "Molecular modeling of the interface of an egg yolk protein-based emulsion"

Dataset for figures of upcoming article "Molecular modeling of the interface of an egg yolk protein-based emulsion" submitted to the journal "Physics of Fluids". dimerApovitellenin1_AA.pdb is the all-atom protein structure file used in MD simulations. DPDparameters.csv is the parametrization file used in DPD simulations.

opencc-by-4.0Nov 2021View details →

zenodo40/100

Data of curvature model for the study of nanoparticle size effects on amyloid fibril stability and molecular dynamics simulations data

The data provided refer to our published article: T. John, J. Adler, C. Elsner, J. Petzold, M. Krueger, L.L. Martin, D. Huster, H.J. Risselada, B. Abel, Mechanistic insights into the size-dependent effects of nanoparticles on inhibiting and accelerating amyloid fibril formation, J. Colloid Interface Sci. 622 (2022), 804–818. <a href="https://doi.org/10.1016/j.jcis.2022.04.134">https://doi.org/10.1016/j.jcis.2022.04.134</a> This article is accompanied by a 'Data in Brief' article that explains in more detail the use of the curvature model and our molecular dynamics (MD) simulations: T. John, L.L. Martin, H.J. Risselada, B. Abel, Curvature model for nanoparticle size effects on peptide fibril stability and molecular dynamics simulation data, Data Brief 45 (2022), 108598. <a href="https://doi.org/10.1016/j.dib.2022.108598">https://doi.org/10.1016/j.dib.2022.108598</a>

opencc-by-4.0Apr 2022View details →

zenodo40/100

Molecular similarity perception based on machine-learning models

Molecular similarity is an particularly important notion for chemical legislation, specifically in the evaluation process for orphan drugs (i.e., drugs for rare diseases). A new molecule needs to be dissimilar from any other existing drug for a given disease to be assigned the financially advantageous status of orphan drug. Currently, there are many ways to define whether two molecules are similar or dissimilar. Thus far, the European Medicines Agency has used experts majority voting on discretional judgments of similarity when assessing new drugs for rare diseases. The decision of individual expert whether two compounds are similar is inherently subjective, depending on factors such as gender, age, state of mind, and previous experiences. It is therefore desirable, in this context, to benefit from an objective measure of similarity. To answer this need, we report a new dataset of molecular similarity assessments, that includes complex and difficult similarity scenarios. As a result, we propose new and improved models for similarity-prediction procedures, including 3D properties. These models are publicly available: <a href="https://chematlas.chimie.unistra.fr/ReadySim/">https://chematlas.chimie.unistra.fr/ReadySim/</a>. Software, 3D structures and pictures are available in the git related to this deposit: <a href="https://github.com/enricogandini/paper_similarity_prediction.git">https://github.com/enricogandini/paper_similarity_prediction.git</a> The deposit contains two files. <ul> <li>original_training_set.csv: this is one of the dataset published initially in [doi: 10.1186/1758-2946-6-5].</li> <li>new_dataset.csv: result from a new survey organized in 2020</li> </ul> The columns are the following: <ul> <li>id_pair: unique identifier of the compound pair</li> <li>curated_smiles_molecule_a: first compound of the pair</li> <li>curated_smiles_molecule_b: second compound of the pair</li> <li>tanimoto_cdk_Extended: ECFP similarity measure</li> <li>TanimotoCombo: ComboScore similarity measure</li> <li>pchembl_distance: difference of activity of the compound pair</li> <li>target_name: protein to which the compound pair is binding</li> <li>simil_2D: similar based on ECFP (0 or 1)</li> <li>simil_3D: similar based on ComboScore (0 or 1)</li> <li>dissimil_2D: dissimilar based on ECFP (0 or 1)</li> <li>dissimil_3D: dissimilar based on ComboScore (0 or 1)</li> <li>pair_type: pairs are classified based on ECFP and ComboScore as similar or dissimilar in 2D and 3D - Sim2DSim3D, Sim2DDis3D, Dis2D,Sim3D, Dis2DSim3D</li> <li>n_answers: number of answers from experts</li> <li>n_similar: number of answers labeling the pair as similar compounds</li> <li>frac_similar: n_similar/n_answers</li> </ul>

openmit-licenseApr 2022View details →

zenodo40/100

Molecular Models of hematite, goethite, kaolinite, and quartz surfaces

Molecular models for hematite, quartz, goethite and kaolinite surfaces according to different terminations.

openother-openJun 2022View details →

zenodo40/100

Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites: supplementary information and dataset

Abstract: (from [1]) <blockquote> The addition of nano-sized filler particles enhances the mechanical performance of polymers. The resulting properties of the polymer nanocomposite depend on a complex interplay of influence factors such as material pairing, filler size, and content as well as filler-matrix adhesion. As a complement to experimental studies, numerical methods, such as molecular dynamics (MD), facilitate an isolated examination of the individual factors in order to understand their interaction better. However, particle-based simulations are, in general, computationally very expensive, rendering a thorough investigation of nanocomposites’ mechanical behavior both expensive and time-consuming. Therefore, this paper presents a fast coarse-grained MD model for a generic nanoparticle-reinforced thermoplastic. First, we examine the matrix and filler phase individually, which exhibit isotropic elasto-viscoplastic and anisotropic elastic behavior, respectively. Based on this, we demonstrate that the effect of filler size, filler content, and filler-matrix adhesion on the stiffness and strength of the nanocomposite corresponds very well with experimental findings in the literature. Consequently, the presented computationally efficient MD model enables the analysis of a generic polymer nanocomposite. In addition to the obtained insights into the mechanical behavior, the material characterization provides the basis for a future continuum mechanical description, which bridges the gap to the engineering scale.  </blockquote>   Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All MD simulations were performed with LAMMPS [2], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [3] Post-processing Matlab R2019b Evaluation of polymer entanglements with Z1-Algorithm [4]   License: Creative Commons Attribution 4.0 International   Context: Data set supplementing  journal paper: [1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, 2022, 16. This dataset contains the results presented in [1] and the necessary data to obtain those as well as supplementary information. Content: supplementary material: supplementary_information.pdf data:     folder names vary depending on the context, explained in the following:   01_matrix <ul> <li> 01_equilibration sample equilibration to different temperatures nomenclature: equil_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>[-<batch_ID>] <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.1-1.0 </li> <li> batch_ID: 2-5  </li> </ul> </li> <li> 02_temperature_dependence uniaxial tension simulations to identify temperature dependence nomenclature: 01_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.1-1.0 </li> </ul> </li> <li> 03_directional_dependence uniaxial tension simulations to prove isotropy in Y and Z direction; X direction in 04_rate_dependence nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-<batchID> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-5 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 04_rate_dependence uniaxial tension simulations to identify strain rate dependence nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>[-<batchID>] <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 05_cyclic_loading sinusoidal uniaxial deformation nomenclature: 05_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4 </li> <li> strain_amplitude: 0.01, 0.05, 0.15, 0.2 </li> </ul> </li> <li> 06_relaxation relaxation subsequent to time-proportional deformation nomenclature: 07_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude>_relax <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4 </li> <li> strain_amplitude: 0.01, 0.05, 0.15, 0.2 </li> </ul> </li> <li> 07_simple_shear time-proportional simple shear deformation with different strain rates nomenclature: SS_P2VPSi-rate_<strain_rate>-<batchID> <ul> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 08_large_deformation uniaxial deformation up to 100% strain nomenclature: 02_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperatur>-strain_<max_strain> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> max_strain: 1 </li> </ul> </li> </ul> 02_filler <ul> <li> 01_Silica_equilibration sample equilibration </li> <li> 02_time_proportional time-proportional uniaxial and simple shear tests nomenclature: Silica_BV-<loadcase>_<direction>-strain_<max_strain>-rate_<strain_rate> <ul> <li> loadcase: uniaxial tension (UT), simple shear (SS) </li> <li> max_strain: 0.1 </li> <li> direction: X, Y, Z (UT); XY, XZ, YZ (SS) </li> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> </ul> </li> <li> 03_time_periodic time-periodic uniaxial and simple shear tests nomenclature: Silica_BV-<loadcase>_<direction>_sin-ampl_<strain_amplitude>-rate_<max_strain_rate> <ul> <li> loadcase: uniaxial tension (UT), simple shear (SS) </li> <li> direction: X, Y, Z (UT); XY, XZ, YZ (SS) </li> <li> strain_amplitude: 0.025 </li> </ul> </li> </ul> 03_composite <ul> <li> 01_equilibration sample equilibration nomenclature: equil_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID> <ul> <li> filler_radius: 2.5-10.0 </li> <li> filler_number: 1-160 (depending on filler_radius) </li> <li> batchID: 1-5 </li> </ul> </li> <li> 02_uniaxial-tension uniaxial tension simulations nomenclature: UT_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID> <ul> <li> filler_radius: 2.5-10.0 </li> <li> filler_number: 1-160 (depending on filler_radius) </li> <li> batchID: 1-5 </li> </ul> </li> <li> 03_filler-maxtrix-adhesion equilibration and uniaxial deformation of samples with mid and weak filler-matrix adhesion (for strong adhesion see 01_equilibration and 02_uniaxial-tension nomenclature: see above </li> <li> 04_IP_equilibration equilibration of samples to evaluate the microstructure for neat polymer and composites with filler radius 2.5-7.5 nomenclature: P2VPSi-<chains>x<chain_atoms>_rNP_<filler_radius>-nNP_<filler_number>_pos_<filler_pos>-<batchID> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> filler_radius: 0 (neat), 2.5, 5.0, 7.5 </li> <li> filler_number: 0 (neat), 1 </li> <li> batchID: 1-20 </li> </ul> </li> </ul>       Each simulation directory contains: <ul> <li> lammps input file (*.in) of the specific simulation </li> <li> data file (*.data) containing the initial sample configuration </li> <li> input.prm: input parameters of the specific simulation (read by the input file) </li> <li> meta.info: meta data of the specific simulation run </li> <li> LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li> thermo_out.Dat: raw output  </li> <li> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter) </li> <li> thermo_out_STD.Dat: standard deviation of raw output </li> </ul> </li> </ul>   Output quantities (columns of *.Dat files): Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1]. <ul> <li> Step: time step  </li> <li> Time: time  </li> <li> TotEng: total energy  </li> <li> PotEng: potential energy </li> <li> KinEng: kinetic energy  </li> <li> E_pair: pair energy  </li> <li> E_bond: bond energy  </li> <li> E_angle: angle energy  </li> <li> E_dihed: dihedral energy  </li> <li> Temp: temperature </li> <li> Press: hydrostatic pressure </li> <li> Pxx: xx component of pressure tensor  </li> <li> Pyy: yy component of pressure tensor  </li> <li> Pzz: zz component of pressure tensor  </li> <li> Pxy: xy component of pressure tensor </li> <li> Pxz: xz component of pressure tensor </li> <li> Pyz: yz component of pressure tensor </li> <li> Volume: volume of simulation box  </li> <li> Lx: box length in x direction   </li> <li> Ly: box length in y direction   </li> <li> Lz: box length in z direction   </li> <li> Density: density   </li> <li> c_RG: radius of gyration scalar  </li> <li> c_RG[1]: squared radius of gyration tensor (xx component)   </li> <li> c_RG[2]: squared radius of gyration tensor (yy component)   </li> <li> c_RG[3]: squared radius of gyration tensor (zz component)   </li> <li> c_RG[4]: squared radius of gyration tensor (xy component)   </li> <li> c_RG[5]: squared radius of gyration tensor (xz component)   </li> <li> c_RG[6]: squared radius of gyration tensor (yz component)   </li> <li> c_bondave[1]: bond energy averaged over all atoms   </li> <li> c_bondave[2]: bond distance averaged over all atoms   </li> <li> c_bondave[3]: squared bond distance averaged over all atoms   </li> <li> c_angleave[1]: angle energy averaged over all atoms   </li> <li> c_angleave[2]: angle averaged over all atoms degree </li> <li> c_angleave[3]: cosine of angle  </li> <li> c_angleave[4]: squared cosine of angle  </li> <li> c_MSD[1]: mean squared displacement x-direction   </li> <li> c_MSD[2]: mean squared displacement y-direction   </li> <li> c_MSD[3]: mean squared displacement z-direction   </li> <li> c_MSD[4]: total mean squared displacement   </li> <li> c_COM[1]: x coordinate of center of mass   </li> <li> c_COM[2]: y coordinate of center of mass   </li> <li> c_COM[3]: z coordinate of center of mass   </li> <li> v_strain_xx: xx component of engineering strain tensor    </li> <li> v_strain_yy: yy component of engineering strain tensor     </li> <li> v_strain_zz: zz component of engineering strain tensor     </li> <li> v_vMisesequivstress: von Mises equivalent stress  </li> <li> v_Cauchy_xx: xx component of stress tensor   </li> <li> v_Cauchy_yy: yy component of stress tensor </li> <li> v_Cauchy_zz: zz component of stress tensor </li> <li> v_Cauchy_xy: xy component of stress tensor  </li> <li> v_Cauchy_xz: xz component of stress tensor  </li> <li> v_Cauchy_yz: yz component of stress tensor  </li> <li> v_strain_xy: xy component of engineering strain tensor    </li> <li> v_strain_xz: xz component of engineering strain tensor    </li> <li> v_strain_yz: yz component of engineering strain tensor    </li> </ul>   References: [1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, 2022, 16. [2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19. [3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022. [4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, Zenodo, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a>

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Fig. 5 in Phylogeography and potential glacial refugia of terrestrial gastropod Faustina faustina (Rossmässler, 1835) (Gastropoda: Eupulmonata: Helicidae) inferred from molecular data and species distribution models

Fig. 5 BEAST phylogenetic tree based on the COI sequences. Node values indicate divergence estimated in MYA

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Fig. 7 in Phylogeography and potential glacial refugia of terrestrial gastropod Faustina faustina (Rossmässler, 1835) (Gastropoda: Eupulmonata: Helicidae) inferred from molecular data and species distribution models

Fig. 7 Areas of climatic stability over time periods from the LGM through the present, based on summed climatic suitability models for the LGM, mid-Holocene, and present day for three differed GCMs. Stability increase from red to yellow color. White-filled areas show the

opencc-by-4.0Oct 2020View details →

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Research data supporting "Multiscale Molecular Modelling of ATP-Fueled Supramolecular Polymerisation and Depolymerisation"

Raw research data supporting the publication Perego C. et al., ChemSystemsChem 2021, DOI: <a href="https://doi.org/10.1002/syst.202000038">https://doi.org/10.1002/syst.202000038</a>

opencc-by-4.0Jun 2021View details →

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Molecular Determinants and Pharmacological Analysis for a Class of Competitive Non-transported Bicyclic Inhibitors of the Betaine/GABA Transporter BGT1: Modeling Data

This archive contains the modeling data for the study <a href="https://www.frontiersin.org/articles/10.3389/fchem.2021.736457/full">"Molecular Determinants and Pharmacological Analysis for a Class of Competitive Non-transported Bicyclic Inhibitors of the Betaine/GABA Transporter BGT1"</a> (doi: 10.3389/fchem.2021.736457). The following data sets are available: <ul> <li>Induced fit docking results of all mentioned compounds in the study:  ifd_hBGT1_occ_clustering_all_compounds.zip  </li> <li>MD simulations of bicyclo-GABA and compound 1 (100ns, 3 replica): MD_simulation_bicyclo-GABA_run1.zip MD_simulation_bicyclo-GABA_run2.zip MD_simulation_bicyclo-GABA_run3.zip MD_simulation_cmd1_run1.zip MD_simulation_cmd1_run2.zip MD_simulation_cmd1_run3.zip</li> </ul> A detailed description of the methods is available in the aforementioned publication.   The compound numbering in the uploaded files differs from the compound numbering in the mentioned study:   study / upload bicyclo-GABA / cmd4 1 / IIa 2 /8-2 3 / 8-3 4a / 7-1 4b / 7-2 4c / 7-3

opencc-by-4.0Sep 2021View details →

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Allen Brain Atlas

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DANDI Archive for NWB datasets

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electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

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behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record