Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
40
datasets available to search
ShareScore release 0.9.0
Dataset results
40 results for “Chemical Space”
Exploring chemical space in the search for improved Azoheteroarene-based photoswitches
<p>In the quest for improved photo switches, azoheteroarenes have emerged as a potential alternative to azobenzene. However, to date the number and types of these species that have subjected to study is insufficient to provide an in-depth understanding of the photochemical effects brought about by different substituents. Here, we computationally screen the optical properties and thermal stabilities of 512 azoheteroarenes that consist of eight different N-containing heteroarenes combined with 64 substitution patterns. The most promising compounds are identified and their properties rationalized based on the nature of the azoheteroarene core and the location and type of substitution patterns.</p>
Suplementary material: Towards uncoding hepatotoxicity of approved drugs through navigation of multiverse and consensus chemical spaces
<p>Supplementary material: "Towards uncoding hepatotoxicity of approved drugs through navigation of multiverse and consensus chemical spaces"</p>
Data files for:Critical assessment of the chemical space covered by LC-HRMS non-targeted analysis
<p>This upload contains the data for the review: "Critical assessment of the chemical space covered by LC-HRMS non-targeted analysis".</p> <p>All the files needed to run the code uploaded to GitHub (https://github.com/tobihul/CEC_review_code) can be found here.</p> <p>Included is: </p> <ul> <li>All 2657 structures found in the studied papers with their CID, InChIKey, and SMILES and whether they can be found in MassBank</li> <li>All the experimental parameters retrieved for each study in each category along with the general scope of each study</li> <li>The file with the CID, MW, XLogP3 and experimental parameters for each of the 61 papers</li> <li>The CSV file containing all classes of each of the compounds from the papers</li> <li>The CSV with all of the structures used to plot the chemical space of NORMAN SusDat (their CIDs)</li> </ul> <p> </p> <p> </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>
Expanding the chemical space using a Chemical Reaction Knowledge Graph
<p>This contains:</p><ul><li>the reaction graph dataset used to train the link prediction model</li></ul><p>Homepage: https://github.com/MolecularAI/reaction-graph-link-prediction</p>
Data and code from: SIDERITE: Unveiling Hidden Siderophore Diversity in the Chemical Space Through Digital Exploration
<h1>TMAP of COCONUT database</h1> <p>The script and data used in SIDERITE paper to generate TMAP picture (Figure S3 in supplementart material).</p> <p>Requirement: tmap</p> <p>You can install tmap by conda.</p> <blockquote> <p>conda create -n tmap python=3.7</p> <p>conda activate tmap</p> <p>conda install -c tmap tmap</p> <p>pip install faerun</p> <p>pip install matplotlib</p> <p>conda install scipy</p> <p>conda install -c rdkit rdkit</p> <p>conda install -c conda-forge mhfp</p> </blockquote> <p> </p> <p>Usage: python plot_COCONUT.py</p> <p>Then it will use COCONUT.csv to generate index.html and index.js. Open index.html to see result.</p> <h1>Other large input files</h1> <p>Tanimoto_COCONUT_SIDERITE.xlsx is the input file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/predicted_new/Clustering_coconut.m">SIDERITE/predicted_new/Clustering_coconut.m at main · RuolinHe/SIDERITE</a>.</p> <p> </p> <p>Sid_structure_output3.xlsx is used in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/statistics/Figure1.m" target="_blank" rel="noopener">SIDERITE/statistics/Figure1.m at main · RuolinHe/SIDERITE</a>, <a href="https://github.com/RuolinHe/SIDERITE/blob/main/TAMP/tmap_code.m" target="_blank" rel="noopener">SIDERITE/TAMP/tmap_code.m at main · RuolinHe/SIDERITE</a> and <a href="https://github.com/RuolinHe/SIDERITE/blob/main/siderophore_process/Sid_process_code.m" target="_blank" rel="noopener">SIDERITE/siderophore_process/Sid_process_code.m at main · RuolinHe/SIDERITE</a>.</p> <p> </p> <p>COCONUT4MetFrag_Canonical.xlsx is the input file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/TAMP/tmap_code.m" target="_blank" rel="noopener">SIDERITE/TAMP/tmap_code.m at main · RuolinHe/SIDERITE</a>.</p> <p> </p> <p>COCONUT_r.txt is the output file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/TAMP/tmap_code.m" target="_blank" rel="noopener">SIDERITE/TAMP/tmap_code.m at main · RuolinHe/SIDERITE</a>. and the input file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/predicted_new/isSiderophore1.py" target="_blank" rel="noopener">SIDERITE/predicted_new/isSiderophore1.py at main · RuolinHe/SIDERITE.</a></p> <p> </p> <p>COCONUT4MetFrag.xlsx is the input file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/TAMP/CheckSMILES2.py" target="_blank" rel="noopener">SIDERITE/TAMP/CheckSMILES2.py at main · RuolinHe/SIDERITE</a>.</p> <p> </p>
Data for: Mapping the Limits of Passive Samplers in Water: Chemical Space Coverage Using Nontargeted LC-HRMS Analysis
<p>This dataset provides files for passive samplers nad blanks analyzed by LC-HRMS fullscan DIA MS2.</p> <p>Excel file provides information about passive samplers, sampling site and sample files.</p>
Merging Flow Synthesis and Enzymatic Maturation to Expand the Chemical Space of Lasso Peptides
<p>LC-MS, UHPLC, and LC-IM-MS data of the corresponding publication</p>
Accessible Chemical Space for Metal Nitride Perovskites
<p>Supporting data to manuscript "Accessible Chemical Space for Metal Nitride Perovskites"</p> <p>The folders contain respectively:</p> <ul> <li>25_Candidates_HSE06: The structures of the 25 candidates presented in the manuscript and relaxed with HSE06</li> <li>Competing_phases: The structures of the 86 competing phases considered for thermodynamic stability and relaxed with HSE06</li> <li>Json_summaries: Json files containing all the relevant information. This includes structures and energies for: <ul> <li>the 279 candidates in 15 possible tilts</li> <li>the lowest energy tilted structure for all 279 candidates</li> <li>the intermediate structures found during the phonon mapping process</li> <li>the ten lowest energy structures for each of the 25 candidates obtained with AIRSS</li> <li>the summary of all lowest-energy structures identified in both workflows</li> <li>the materials already reported in literature and recomputed in this work</li> <li>the energy above the hull for all considered structures calculated with HSE06 and PBEsol</li> </ul> </li> <li>Switching_paths: The structures along the switching path and the Born effective charges of the material calculated in the initial configuration</li> </ul>
Supplementary Data for "Exploring the Chemical Space of Metal–Organic Frameworks with rht Topology for High Capacity Hydrogen Storage"
<p>This dataset includes optimization and simulation inputs, optimized structures, building blocks, computed hydrogen uptakes and textural properties of the metal–organic frameworks that correspond with work in "Exploring the Chemical Space of Metal–Organic Frameworks with rht Topology for High Capacity Hydrogen Storage" (DOI: 10.1021/acs.jpcc.4c00638).</p>
PubChem and ChEMBL-series processed dataset used in Exhaustive local chemical space exploration using a transformer model
<p>PubChem and ChEMBL-series processed dataset used in <span>Exhaustive local chemical space exploration using </span><span>a transformer model</span></p>
Resolution limit of data-driven coarse-grained models spanning chemical space
<p>This repository contains all databases referenced in the supporting information of the paper titled "Resolution limit of data-driven coarse-grained models spanning chemical space" by Kiran H. Kanekal and Tristan Bereau.</p>
Traversing Chemical Space with Active Deep Learning for Low-data Drug Discovery
<p>Raw and processed data from LitPCBA used in the paper "Traversing Chemical Space with Active Deep Learning for Low-data Drug Discovery"</p>
Supporting Information for Automated and Efficient Sampling of Chemical Reaction Space
<p>These datasets include structures from normal mode sampling, reaction pathway sampling, and transition states for validation (originally from Grambow et al.), all computed using the ωB97X/6-31G(d) method. The corresponding energies and forces are compiled in the Atomic Simulation Environment database format. </p>
Profiling the REACH-chemical space with Generative Topographic Mapping
<p>The REACH (Registration Evaluation Authorization and restriction of Chemicals) regulation requires from industries the reporting of hazard data for substances placed on the market. Thanks to the registration procedure initiated in 2007, a large REACH database [1] of well defined (eco)toxicological properties has been created. The REACH-chemical space is defined by more than 21’500 compounds registered in the REACH database.</p> <p>Considering the high number of chemicals and endpoints for which experimental data is available, the ability to visualize this chemical space as well as to profile a compound integrating several key properties at once is a growing need.</p> <p>Here, the data distribution in REACH chemical space was visualized and analysed with the help of 2-dimensional Generative Topographic Map (GTM). Similarly to geographical map, on GTM, each object (compound) is visualized as a datapoint. Moreover, compounds possessing similar properties tend to be located in neighbourhood. The third dimension can be added in order to display a distribution of the given (eco)toxicological property (such-called “property landscape”), which can further be used for property assessment of new compounds projected on the map. </p> <p>We report the universal REACH map which accommodates 11 endpoints, covering environmental fate, and (eco)toxicological properties. This map is able to provide predictions for each property, and demonstrates acceptable predictive performance in cross-validation: balanced accuracy ranges from 0.60 to 0.78. Superposition of different property landscapes allows to delineate the “areas of interest” populated by molecules possessing desirable (eco)toxicological profile.</p>
Fig. 11 in Computational insight into the chemical space of plant growth regulators
Fig. 11. The results of testing the discovered hit-molecules (dots within the map) using the developed in silico model (background gradient corresponds to the distribution of Ref. PGRs). This case, the scale at the bottom indicates the number of hits in each node.
Fig. 10 in Computational insight into the chemical space of plant growth regulators
Fig. 10. The discrimination between two agro-categories using Ui: PGRs/pesticides (a), herbicides/PGRs (b).
Fig. 8 in Computational insight into the chemical space of plant growth regulators
Fig. 8. The distributions of PGRs (a), herbicides (b), fungicides (c), and insecticides (d). The scale at the bottom corresponds to the number of compounds; the axes indicate the node coordinates in the lattice; the contours are smoothed.
Fig. 9 in Computational insight into the chemical space of plant growth regulators
Fig. 9. The distributions of the descriptor values within the map for HBA (a) and RBN (b) demonstrate that separation could be achieved with a holistic approach. The scale at the bottom shows the number of the compounds; the axes indicate the node coordinates in the lattice; the contours are smoothed.
Fig. 4 in Computational insight into the chemical space of plant growth regulators
Fig. 4. The diversity in heterocycles for the reference compounds: PGRs (a), the whole reference set (b), herbicides (c).
ScienceDex guides
Understand access before you commit
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.