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41 results for “molecular properties”
Molecular and functional properties of human Plasmodium falciparum CSP C-terminus antibodies
<p>AIRR Community-compliant information comprising all antibodies described in EMBO Mol Med 15:e17454 [DOI:10.15252/emmm.202317454].</p>
Quantifying the light-absorption properties and molecular composition of brown carbon aerosol from sub-Saharan African biomass combustion
<div> <p>Data presented in figures of the peer-reviewed journal article "Quantifying the light-absorption properties and molecular composition of brown carbon aerosol from sub-Saharan African biomass combustion" by Moschos et al.</p> <p>Disclaimer: Please contact the dataset creator prior to inclusion of data in any upcoming publication.</p> </div>
Data and code for behavioral analysis of: Structural and Molecular Properties of Insect Type II Motor Axon Terminals.
<p>Data and code for behavioral analysis of: Structural and Molecular Properties of Insect Type II Motor Axon Terminals.</p> <p>v1.2: typos corrected and all files available in a single .zip file for download</p>
Supporting Data for: A Multimer Embedding Approach for Molecular Crystals up to Harmonic Vibrational Properties
<p>Accurate calculations of molecular crystals are crucial for drug design and crystal engineering. However, periodic high-level density functional calculations using hybrid functionals are often prohibitively expensive for relevant systems. These expensive periodic calculations can be circumvented by the usage of embedding methods in which for instance the periodic calculation is only performed at a lower-cost level and then monomer energies and dimer interactions are replaced by those of the higher-level method. Herein, we extend upon such a multimer embedding approach to enable energy corrections for trimer interactions and the calculation of harmonic vibrational properties up to the dimer level. We evaluate this approach for the X23 benchmark set of molecular crystals by approximating a periodic hybrid density functional (PBE0+MBD) by embedding multimers into less expensive calculations using a generalized-gradient approximation (GGA) functional (PBE+MBD). We show that trimer interactions are crucial for accurately approximating lattice energies within 1 kJ/mol and might also be needed for further improvement of lattice constants and hence cell volumes. Finally, vibrational properties are already very well captured at the monomer and dimer level, making it possible to approximate vibrational free energies at room temperature within 1 kJ/mol.</p><p>This supporting dataset includes results of PBE0+MBD, PBE+MBD, and multimer embedding calculations for the X23 set of molecular crystals. See the included README.md file for more details. The related preprint can be found at <a href="https://doi.org/10.48550/arXiv.2209.02687">https://doi.org/10.48550/arXiv.2209.02687</a>.</p>
Data for "Ultimate molecular mechanical properties of polyolefin chains"
<p>LAMMPS input and data files, force field files, sample simulation outputs, and Jupyter notebooks used for the data analysis.</p>
Extraocular muscle stem cells exhibit distinct cellular properties associated with non-muscle molecular signatures
<p>The muscle stem cell (MuSC) population is recognized as functionally heterogeneous. Cranial muscle stem cells, which originate from head mesoderm, can have greater proliferative capacity in culture and higher regenerative potential in transplantation assays when compared to those in the limb. The existence of such functional differences in phenotypic outputs remain unresolved as a comprehensive understanding of the underlying mechanisms is lacking. We addressed this issue using a combination of clonal analysis, live imaging, and scRNA-seq, identifying critical biological features that distinguish extraocular (EOM) and limb (Tibialis anterior, TA) MuSC populations. Time-lapse studies using a MyogenintdTomato reporter showed that the increased proliferation capacity of EOM MuSCs is accompanied by a differentiation delay in vitro. Unexpectedly, in vitro activated EOM MuSCs expressed a large array of distinct extracellular matrix (ECM) components, growth factors, and signaling molecules that are typically associated with mesenchymal non-muscle cells. These unique features are regulated by a specific set of transcription factors that constitute a coregulating module. This transcription factor network, which includes Foxc1 as one of the major players, appears to be hardwired to EOM identity as it is present in quiescent adult MuSCs, in the activated counterparts during growth and retained upon passages in vitro. These findings provide insights into how high-performing MuSCs regulate myogenic commitment by active remodeling of their local environment.</p>
Data associated to the article "Effects of fluoride salt addition to the physico-chemical properties of the MgCl2-NaCl-KCl heat transfer fluid : a molecular dynamics study"
<p>Contains input file and data used to generate the figures of the article:</p> <p>Effects of fluoride salt addition to the physico-chemical properties of the MgCl<sub>2</sub>-NaCl-KCl heat transfer fluid : a molecular dynamics study</p> <p>Weiguang Zhou, Yanping Zhang, Mathieu Salanne</p> <p>https://chemrxiv.org/engage/chemrxiv/article-details/618e903a2bf8a950c7d98e5d</p> <p>The files <em>data.inpt</em> and <em>runtime.inpt </em>are used to simulate the system using the software MetalWalls</p> <p>The files <em>MgNaKCl.txt, MgNaKClF01.txt, MgNaKClF05.txt, MgNaKClF10.txt, MgNaKClF20.txt</em> contain the computed densities, viscosities and thermal conductivities at various temperatures for several compositions (provided in the header of the files)</p>
High-Throughput Screening of Tribological Properties of Monolayer Films using Molecular Dynamics and Machine Learning: Supplemental Repository
<p>Supplemental repository for the "High-Throughput Screening of Tribological Properties of Monolayer Films using Molecular Dynamics and Machine Learning" article. Contains calculated tribological properties of dual-monolayer systems from Molecular Dynamics (MD) simulation and Machine Learning (ML).</p>
Solution #4 for Predicting Molecular Properties Kaggle Competition
<p>Code and additional data for solution #4 in <a href="https://www.kaggle.com/c/champs-scalar-coupling/overview">Predicting Molecular Properties</a> competition, described in <a href="https://www.kaggle.com/c/champs-scalar-coupling/discussion/106534#latest-613848">#4 Solution [Hyperspatial Engineers]</a>.</p>
Out-of-Distribution Evaluation of Active Learning Pipelines for Molecular Property Prediction Dataset
<p>Data for the paper "Out-of-Distribution Evaluation of Active Learning Pipelines for Molecular Property Prediction"</p>
Structures for "Protonation States of Molecular Groups in the Chromophore-Binding Site Modulate Properties of the Reversibly Switchable Fluorescent Protein rsEGFP2"
<p>Files with atomic coordinates of the structures obtained in geometry optimization of molecular systems within the protein and cluster models. Supplementary materials for the paper "Protonation States of Molecular Groups in the Chromophore-Binding Site Modulate Properties of the Reversibly Switchable Fluorescent Protein rsEGFP2"</p>
Data from: Quantum computation of frequency-domain molecular response properties using a three-qubit iToffoli gate
<p>The quantum computation of molecular response properties on near-term quantum hardware is a topic of substantial interest. Computing these properties directly in the frequency domain is desirable, but the circuits require large depth if the typical hardware gate set consisting of single- and two-qubit gates is used. Here, we report the application of a high-fidelity multipartite gate, the iToffoli gate, to the computation of frequency-domain response properties of diatomic molecules. The iToffoli gate enables a ~50% reduction in circuit depth and ~40% reduction in circuit execution time compared to the traditional gate set. We show that the molecular properties obtained with the iToffoli gate exhibit comparable or better agreement with theory than those obtained with the native CZ gates. Our work is among the first demonstrations of the practical usage of a native multi-qubit gate in quantum simulation, with diverse potential applications to near-term quantum computation.</p>
Data from: Quantum computation of frequency-domain molecular response properties using a three-qubit iToffoli gate
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Extraocular muscle stem cells exhibit distinct cellular properties associated with non-muscle molecular signatures
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DrugMol3D: An Expanded Collection of Molecular Data for Optimized Drug Structures and Descriptive Properties
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Metadata - Direct C−H Arylation of Dithiophene-Tetrathiafulvalene: Tuneable Electronic Properties and 2D Self-Assembled Molecular Networks at the Solid/Liquid Interface
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Influence of Molecular Hydrogen on Bulk and Interfacial Properties of Three Imidazolium-Based Ionic Liquids by Experiments and Molecular Dynamics Simulations
<p>Original materials including both experimental and simulation data.</p> <p>Pictures (.bmp) of pendant drop method and the surface light scattering signals (.asc).<br>Raw simulation data for the surface tension. The topology files and all the final structure files(.gro) are included for the two systems at all studied temperature and pressures.</p>
Chemperium database for: Geometric Deep Learning for Molecular Property Predictions with Chemical Accuracy Across Chemical Space
<p>The dataset and trained models for the submitted manuscript "Geometric Deep Learning for Molecular Property Prediction with Chemical Accuracy Across Chemical Space"</p> <p>The trained models can be used in combination with the predict module in github.com/mrodobbe/chemperium. More information in README.md.</p> <p><em>When using these datasets, refer directly to the manuscript: https://doi.org/10.1186/s13321-024-00895-0 </em></p>
Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties using Machine Learning
<p>This dataset accompanies a study that investigates the use of machine learning (ML) approaches for predicting seven different physical properties of 140 binary copolymers. Two computational methods were employed: a random forest (RF) model based on molecular descriptors and a Graph Neural Network (GNN) using 2D polymer graphs. These methods were applied in both single- and multi-task settings to explore the strengths of each approach in capturing various polymer properties.</p> <p>The dataset includes two files:</p> <ol> <li> <p><strong>Dataset.xlsx</strong>: Contains the following information for each of the 140 copolymers:</p> <ul> <li>Polymer names</li> <li>SMILES notation of the monomers</li> <li>Fraction of monomers in each copolymer</li> <li>Simulated values (calculated using molecular dynamics simulation) and experimental values of various physical properties, including density, specific heat capacity at constant pressure ad volume ,radius of gyration, linear expansion coefficient, volume expansion coefficient, and bulk modulus .</li> </ul> </li> <li> <p><strong>Descriptors.xlsx</strong>: Provides the molecular descriptors calculated using PaDEL-Descriptor software, which were used as input for the RF model to predict polymer properties.</p> </li> </ol> <p>This work provides insight into the comparative strengths of descriptor- and graph-based representations in machine learning models for predicting material properties. It also highlights the importance of selecting appropriate molecular representations based on the nature of the properties being predicted.</p>
An Interpretable 3D Multi-Hierarchical Molecular Hybrid Representation for Environmental, Health, and Safety Properties Prediction
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.
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.