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1,805 results for “molecule”
Experimental n-octanol/water Partition/Distribution Coefficients Database for Small Molecules
<p>We critically compiled experimental values of log<em>P</em><sub>N, </sub>pK<sub>a</sub>, log<em>P</em><sub>I</sub>, and log<em>D</em><sub>pH</sub> of 225 entries based on earlier literature reports. The experimental techniques of log<em>P</em><sub>N</sub> , log<em>D</em><sub>pH</sub>, and log<em>P</em><sub>I</sub> for each molecule were thoroughly revised and added to the database. The molecules were classified between acids and bases according to their functional groups and pK<sub>a</sub> values. </p> <p>If you use this database please cite our <a href="https://doi.org/10.1002/cphc.202300548">ChemPhysChem</a> paper</p>
Nonlocal machine-learned exchange functional for molecules and solids
<p>This dataset supplements the journal article "Nonlocal machine-learned exchange functional for molecules and solids," published in Physical Review B: <a href="https://doi.org/10.1103/PhysRevB.110.075130">DOI:10.1103/PhysRevB.110.075130</a>. It contains the machine-learned functionals developed in the study (which can be used with the <a href="https://github.com/mir-group/CiderPressLite">CiderPressLite code</a>), along with additional data and results from the study.</p> <p>Please see the README.md file for more details on the dataset, and refer to the original paper for details on methodology and funding acknowledgments.</p>
Dataset supporting the paper "Bipolar single-molecule electroluminescence and electrofluorochromism. Physical Review Research 5, 033027 (2023)"
<p>Dataset corresponding to theoretical calculations in the paper "Bipolar single-molecule electroluminescence and electrofluorochromism. Physical Review Research 5, 033027 (2023)" DOI: https://doi.org/10.1103/PhysRevResearch.5.033027</p> <p>Please cite as:</p> <p>Tzu-Chao Hung, Roberto Robles, Brian Kiraly, Julian H. Strik, Bram A. Rutten, Alexander A. Khajetoorians, Nicolas Lorente and Daniel Wegner. Dataset supporting the paper "Bipolar single-molecule electroluminescence and electrofluorochromism. Physical Review Research 5, 033027 (2023)" DOI:10.5281/zenodo.13768737</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:</p> <p>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (https://jp-minerals.org/vesta/en/).</p> <p>.agr: grace files (https://plasma-gate.weizmann.ac.il/Grace/).</p> <p>Image files in png format.</p>
Dataset supporting the paper "Single-Spin Sensing: A Molecule-on-Tip Approach. ACS Nano 18, 13829 (2024)"
<p>Dataset corresponding to theoretical calculations in the paper "Single-Spin Sensing: A Molecule-on-Tip Approach" ACS Nano 18, 13829 (2024) DOI: https://doi.org/10.1021/acsnano.4c02470</p> <p>Please cite as:</p> <p>Alex Fétida, Olivier Bengone, Michelangelo Romeo, Fabrice Scheurer, Roberto Robles, Nicolás Lorente, and Laurent Limot. Dataset supporting the paper "Single-Spin Sensing: A Molecule-on-Tip Approach. ACS Nano 18, 13829 (2024)" DOI: 10.5281/zenodo.13774118</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:</p> <p>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (https://jp-minerals.org/vesta/en/).</p> <p>.agr: grace files (https://plasma-gate.weizmann.ac.il/Grace/).</p> <p>Image files in png format.</p>
Database of small molecule X-ray absorption spectra, featurized structures, and neural network ensembles
<p>Companion data for arXiv preprint <em>Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles</em> (<a href="https://arxiv.org/abs/2210.00336">https://arxiv.org/abs/2210.00336</a>), by Animesh Ghose, Mikhail Segal, Fanchen Meng, Zhu Liang, Mark S. Hybertsen, Xiaohui Qu, Eli Stavitski, Shinjae Yoo, Deyu Lu & Matthew R. Carbone.</p> <p><strong>Included</strong></p> <ul> <li>*-XANES-*.tar.bz2: raw input/output files for all molecular simulations used in the work. These inputs and outputs correspond to the structural data in the QM9 dataset.</li> <li>ml_ready.tar.bz2: machine learning-ready data (featurized spectra). Used as input to the neural network ensembles.</li> <li>XANES-220712-ACSF-*.tar.bz2: neural network ensembles used in this work.</li> </ul> <p><strong>Notes</strong></p> <ul> <li>The FEFF9 code [J. J. Rehr, J. J. Kas, F. D. Vila, M. P. Prange, and K. Jorissen, <em>Phys. Chem. Chem. Phys.</em> <strong>12</strong>, 5503 (2010)] was used to generate all X-ray absorption near-edge structure (XANES) spectra.</li> <li>All molecular structures were sourced from the QM9 database [R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld, <em>Sci. Data</em> <strong>1</strong>, 1 (2014)].</li> </ul> <p><strong>Funding</strong></p> <p>This research is based upon work supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. This research also used theory and computational resources of the Center for Functional Nanomaterials, which is a U.S. Department of Energy Office of Science User Facility, and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under Contract No. DE-SC0012704.</p>
DECIMER - Hand-drawn molecule images dataset
<p><strong>DECIMER - Hand-drawn molecule images dataset</strong></p> <p>The translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available.</p> <p>Here we present DECIMER - Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.</p>
Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset
<p><b>Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset.</b></p><p>Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.</p>
Extreme dynamics of a small molecule in its bound state with an intrinsically disordered protein
<p>These data support the manuscript entitled "Extreme dynamics of a small molecule in its bound state with an intrinsically disordered protein" by Heller, Shukla, Figueiredo, and Hansen.</p><p>This data should be used with the code provided on GitHub at https://github.com/hansenlab-ucl/R2_IDP_small_mol. Once downloaded, this directory should be extracted using the following command:</p><p> tar -xzvf Data.tar.gz</p><p>The directory should be saved with the name 'Data' placed in the same directory as the GitHub README.md file.</p><p><strong>This dataset contains: </strong><br><i>Nuclear Magnetic Resonance (NMR) spectroscopy data files (.ft2 format) including: </i></p><p>* 1H 1D ligand-detected chemical shift titration of 5-fluoroindole (50 uM) with increasing concentrations of the protein, non-structural protein 5A, domains 2 and 3 (NS5A-D2D3), in 1H_1D_ft2_data/</p><p>* 1H pseudo-2D Diffusion Ordered SpectroscopY (DOSY) data of 5-fluoroindole (50 uM) with and without NS5A-D2D3 (75 uM) in 1H_DOSY_data/</p><p>* 1H-15N Heteronuclear Single Quantum Coherence (HSQC) measurements of NS5A-D2D3 (40 uM) in the absence and presence of 5-fluoroindole (160 and 320 uM) in 1H_15N_HSQC_ft2_and_metadata/</p><p>* 19F 1D ligand-detected chemical shift titration of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_1D_ft2_data/</p><p>* 19F pseudo-2D ligand-detected longitudinal (spin-lattice, R1,eff) relaxation titration data of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_R1eff_ft2_data/</p><p>* 19F pseudo-2D ligand-detected longitudinal (spin-spin, R2,eff) relaxation titration data of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_R2eff_ft2_data/</p><p><i>Circular Dichroism (CD) data files (.txt format) including: </i></p><p>* CD measurements of NS5A-D2D3 at increasing concentrations in CD_data/no_molecule/</p><p>* CD measurements of NS5A-D2D3 with and without the small molecule, 5-fluoroindole CD_data/with_molecule/</p><p><i>Metadata </i></p><p>* Metadata from the Biological Magnetic Resonance Data Bank (https://bmrb.io/) used to determine scaling factors for the calculation of chemical shift perturbations in 1H_15N_HSQC_ft2_and_metadata/</p>
Ha-SingleMoleculeLab's data for publication: Linking folding dynamics and function of SAM/SAH riboswitches at the single molecule level
<p>This upload is the raw data that support our findings sent to review on Nucleic Acids Research, corresponding to each individual figure. The paper title is " Linking folding dynamics and function of SAM/SAH riboswitches at the single molecule level".</p>
RMG-DB-11: Enumerating Reaction Space for Small Molecule Chemistry
<p>This repository presents approximately 750 million atom-mapped reaction SMILES. Reactions are generated by applying templates from the Reaction Mechanism Generator (RMG) database to a subset of the species from GDB11. Thus, we refer to this dataset as RMG-DB-11 i.e., the Reaction Mechanism Generator Database whose species contain up to 11 heavy atoms. All SMILES have been canonicalized by RDKit. All reactions are labeled with their corresponding RMG template.</p> <p>This data serves as a crucial starting point for quantitative predictive chemistry. Many methods that search for transition state structures require atom-mapped SMILES, which this repository provides. This data is also well-suited for unsupervised pre-training of various machine learning models.</p> <p>To parse the data with Python, start with <em>import pandas as pd</em>. Reactions with 1-8 heavy atoms can be parsed using the following code snippet: <em>pd.read_csv(<filepath>)</em>. Reactions with 9 heavy atoms can be parsed using <em>pd.read_pickle(<filepath>, compression='zip')</em>. The file names below include the word "zip" as a helpful hint to use the compression argument. Due to the large number of reactions with 10 and 11 heavy atoms, these are split into smaller chunks. First untar the file using <em>tar -xvf <tar_archive></em> to obtain several zipped pickle files that can each be parsed using the same method as with 9 heavy atoms.</p>
An association sequence suitable for producing ground-state RbCs molecules in optical lattices
<p>The data that support the findings of this study are uploaded here. All the data files are self-explanatory. They contain individual column names. </p> <p>The experimental data for Fig. 4(a) are in Fig4a_experimental_data.csv under the folders Fig_4>Fig_4a. To convert to binding energy there is a fitting algorithm in the Python code Feshbach_Fit.py .</p>
Microsecond ALEX FRET analysis notebook using FRETbursts - corrections, FRET burst analysis of recurring molecules, FCS, 2CDE & BVA
<p>The herein Python notebook uses FRETbursts (download from here: https://github.com/tritemio/FRETBursts) to show how to analyze microsecond alternating laser excitation (usALEX) confocal-based FRET measurements of freely diffusing single molecules. It includes a step-by-step calculation and implementation of correction factors, donor fluorescence leakage to the acceptor detection channel (Lk), acceptor fluorescence caused by acceptor excitation by the laser intended for donor excitation (Dir), the imbalance in acceptor/donor fluorecence quantum yields and detection efficiencies (Gamma) and the imbalance in donor/acceptor excitation yields (Beta). The notebook implments a global Gamma correction, assuming the Gamma correction factor is constant for all FRET populations, based on the procedure from Lee et al. 2005. Burst search for showing the FRET population is a dual-channel burst search. After correction, the corrected FRET histogram is presented (after burst selection takes into account Beta & Gamma corrected burst sizes). We also present analysis of bursts from recurring molecules, as well as the FCS (a bit irrelevant here, due to the lasr alternation in microeconds), 2CDE & BVA plots, helping in identifying whether a FRET population is a time average of FRET states, occurring faster then molecular diffusion time, or wheather the FRET population is static and represents a single conformational state. The sample data is a result of measurement of 50 pM of hairpin 3 presented in Tsukanov et al. 2013, labeled with ATTO dyes (ATTO 550 & ATTO 647N as donor and acceptor dyes, respectively) - 532 & 640 nm cw excitation, with an alternation period of 50 microseconds. </p>
London Dispersion Governs the Interaction Mechanism of Small Polar and Non-Polar Molecules in Metal-Organic Frameworks
<p>Raw data set relating to publication.</p>
COVID 19 SARS COV2 targets and small molecule data including insilico analysis
<p>Welcome to the repository for the COVID-19 research data.</p> <p>Corresponding Author: Girinath G. Pillai and few experts</p> <p>Co-authors: Team of experts, scholars and students</p> <p>To join dedicated Slack Discussion : <a href="https://join.slack.com/t/nyroindia/shared_invite/zt-ejes216c-QZzEK_G5tNKIjewbVj2IPA">https://join.slack.com/t/nyroindia/shared_invite/zt-ejes216c-QZzEK_G5tNKIjewbVj2IPA</a></p> <p>We commit to conduct research analysis and all the findings and data will be open and anyone can use or help us improve the data.</p> <p>The parameters for checkpoints are:</p> <p>A) Pharmacophore Modelling - i) generate pharmacophore reference maps from XRay crystal geometry, ii) Generate all possible conformers of the dataset molecules for screening.</p> <p>B) Virtual Screening - i) highest docking score within the dataset, ii) lowest clashes (interligand or intraligand), iii) interactions with key amino acid residues based on literature reports, PROSITE server and pocket finding algorithm like DoGSite or CASTp iv) optimal LE values and v) satisfactory interactions between small molecules and amino acids.</p> <p>C) Selection - i) binding affinity range predictions, lowest among the dataset, ii) free binding energy calculation considering desolvation terms, lowest among the dataset and iii) torsion analysis - coverage of bonds in CSD database.</p> <p>D) Optimization - i) pharmacokinetic properties to be generated from selected hits and an optimal balance of properties to be considered for candidate selection criteria.</p> <p>E) For novel lead molecules - i) chemical space exploration on building blocks could be carried out, ii) on-demand synthesis and procurement.</p> <p>If you prefer you could always cite <a href="https://github.com/giribio/COVID19">https://github.com/giribio/COVID19</a></p> <p>Feel free to create any issues in Github or feel free to contact me via Slack for any queries.</p> <p>Thanks and let us fight against COVID-19 in all possible ways.</p>
Drug molecules binding to Covid-19 main protease
<p>A comparative look at where drug molecules bind to the main protease of Covid-19 depicted by interactive raytracing in the UnityMol software. The visualization is inspired by the animation of small molecules in 92 protein databank structures (<a href="https://www.rbvi.ucsf.edu/chimerax/data/sars-protease-may2020/">https://www.rbvi.ucsf.edu/chimerax/data/sars-protease-may2020/</a>) prepared by the ChimeraX team.</p>
QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
<p>Here, we introduce QM7-X, a comprehensive dataset of > 40 physicochemical properties for ~4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures---comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-trans-and conformational isomers)---as well as 100 non-equilibrium structural variations thereof to reach a total of ~4.2 M molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly converged dataset of quantum-mechanically computed physical and chemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing <em>in silico</em> design of molecules with targeted properties.</p> <p>The dataset is provided in eight HDF5 based files (compressed in .XZ files). One can also find here a README file with technical usage details and examples of how to access the information stored in the dataset (see createDB.py). </p> <p>*The paper explaining the generation of data stored in QM7-X can be found in <em>Sci Data</em> 8, 43 (2021). DOI: 10.1038/s41597-021-00812-2 . arXiv: https://arxiv.org/abs/2006.15139 .</p>
Dataset: "Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification"
<p>The SQLite database contains the pre-computed tandem mass spectra (MS2) and retention order scores used for the experiments in the publication: "<a href="https://doi.org/10.1093/bioinformatics/btaa998">Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification</a>" by Bach et al. (2020).</p> <p>A detailed description of the database structure is given in the 'README.md' and can also be found in the <a href="https://github.com/aalto-ics-kepaco/msms_rt_score_integration/tree/master/data">code-repository associated with the publication</a>. The database layout is illustrated in the 'db_layout.png' file.</p> <p>The SQLite file 'ms_and_rt_score_DB_bach_etal_2020.db.gz' is compressed using <a href="https://en.wikipedia.org/wiki/Gzip">gzip</a>.</p>
Modeling sympathetic cooling of molecules by ultracold atoms: supporting data
<p>Data used in preparation of the paper "Modeling sympathetic cooling of molecules by ultracold atoms", authored by Jongseok Lim, Matthew D. Frye, Jeremy M. Hutson and M. R. Tarbutt.</p> <p>There are four different types of data:</p> <p>(1) Tables of total cross sections versus collision energy for collisions of CaF with Li and Rb for various values of s-wave scattering length (see figure 1)</p> <p>(2) Tables of differential cross sections versus energy for collisions of CaF with Li and Rb for various values of s-wave scattering length. The differential cross sections are given as cumulative distribution functions.</p> <p>(3) Simulated kinetic energy distributions at 1s intervals for sympathetic cooling of CaF with Li and Rb for various values of s-wave scattering length (see figures 5 and 6).</p> <p>(4) Simulated kinetic energy distributions at 1s intervals for sympathetic cooling of CaF with Rb with various evaporative cooling ramps applied to the Rb (see figure 13).</p>
A survey of the sorghum transcriptome using single-molecule long reads
<p>Alternative splicing and alternative polyadenylation (APA) of pre-mRNAs greatly contribute to transcriptome diversity, coding capacity of a genome and gene regulatory mechanisms in eukaryotes. Second-generation sequencing technologies have been extensively used to analyze transcriptomes. However, a major limitation of short-read data is that it is difficult to accurately predict full-length splice isoforms. Here we sequenced the sorghum transcriptome using Pacific Biosciences single molecule real time long-read isoform sequencing and developed a pipeline called TAPIS (Transcriptome Analysis Pipeline for Isoform Sequencing) to identify full-length splice isoforms and APA sites. Our analysis reveals transcriptome-wide full-length isoforms at an unprecedented scale with over 11,000 novel splice isoforms. Additionally, we uncover APA of ~11,000 expressed genes and more than 2,100 novel genes. These results greatly enhance sorghum gene annotations and aid in studying gene regulation in this important bioenergy crop. The TAPIS pipeline will serve as a useful tool to analyze Iso-Seq data from any organism.</p>
Optimized geometries for selected ions of ionic liquids and small molecules
<p>The geometries of these selected chemical entities were optimized at the ab initio or semiempirical levels of theory. They can be conveniently used to create more complicated systems through combining species like the free PACKMOL software offers. </p>
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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.