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.
62
datasets available to search
ShareScore release 0.9.0
Dataset results
62 results for “Bioactive compounds”
Bioactivity of small-molecule compounds against Haemonchus contortus
<div> <div> <div> <p>This dataset of small-molecule compounds and their effects on <em>H. contortus </em>was assembled based on the results obtained from screening two compound libraries (Medicines for Malaria Venture Pathogen Box, Compounds Australia Open Scaffolds set) to assess the effect of compounds on the motility of exsheathed third-stage larvae (xL3) of <em>H. contortus </em>(Preston et al., 2016, 2017). Additionally, select literature data were included to augment the in-house generated data.</p> </div> </div> </div>
Chemical structures, Cell Painting and transcriptional profiles for compound bioactivity prediction.
<p>This is the related data, both input and produced for the paper <a href="https://doi.org/10.1101/2020.12.15.422887">"Predicting compound activity from phenotypic profiles and chemical structures"</a>.</p> <p>This data can be merged with <a href="https://github.com/CaicedoLab/2023_Moshkov_NatComm">paper's GitHub repository</a> for reproduction.</p> <p>Folders and files and are described below:</p> <pre><code>├── assay_data ├── assay_matrix_discrete_270_assays.csv Assay matrix with hits for assays (270) and compounds (16170). Note that this is the final file that we used to produce splits. ├── assay_metadata.csv Assay metadata ├── broad_ids.txt List of broad ids used in this study. That is an unfiltered list of compounds required by some analysis scripts. ├── smiles.txt Same as broad_ids.txt, but SMILES strings. ├── feature_data (for 16978 compounds, can be masked with ./misc/compounds16978to16170.npy) ├── cp.npz Classical chemical features ├── ge.npz Gene expression features ├── ge_scale.npz Gene expression scaled features ├── mo.npz Morphology features (not batch corrected) ├── mobc.npz Morphology features (batch corrected) ├── misc ├── compound_analysis.npz Compounds in the dataset identified as PAINS ├── compounds16978to16170.npy Used to filter features from the bigger set of compounds to the final one ├── fingerprints.npz Calculated fingerprints of compounds, those were then used to calculate similarity ├── similarity_fingerprints.npz Similarity matrix for compounds (16978) ├── population_normalized.csv.gz Well-level morphological profiles that were used for batch-correction ├── Table for PUMA Excel file with additional data and plots ├── predictions ├── scaffold_median(mean)_AUC.csv Aggregated median(mean) AUC scores over scaffold-based cross-validation splits. In the paper, median results were reported. ├── scaffold_median(mean)_EF.csv Aggregated median(mean) enrichment factor (EF) over scaffold-based cross-validation splits. In the paper, median results were reported. ├── toprank_chemical_cv{}_hitsnorm.csv Those files are needed to create enrichment plots and contain hit rate and top rank hit rate. ├── Each folder here stands for an experiment type, the number in the folder name is a number of the split. Inside each folder there are the following elements: ├── predictions Folder with predictions for each assay-compound pair for each modality ├── 2022_01_evaluation_all_data.csv File with AUC scores for each assay for the test set in the split ├── 2022_01_evaluation_all_data_EF.csv File with enrichment factor (EF) values for each assay for the test set in the split. Those files exist only for *chemical* folders. ├── assay_matrix_discrete_train(test)_old_scaff.csv Training and test subsets of data for the split. The first column contains broad_id. ├── assay_matrix_discrete_train(test)_old_scaff.csv Same, but SMILES strings in the first column. Those files are used as input to ChemProp! Experiments in this folder are the following: - chemical Scaffold-based 5-fold cross-validation splits, the main results in the paper are reported with this series of experiments. - chemical_bal Same splits as in chemical, but training were run with ChemProp built-in data balancing. - chemical_st Same splits as in chemical, but separate models were trained for each assay. - CV Random 5-fold cross-validation splits. - GE 5-fold cross-validation splits based on same-size clustering of gene expression features. - MOBC 5-fold cross-validation splits based on same-size clustering of batch-corrected morphology features. - random 10 random splits, ~80% of compounds in the training set and the rest in the test set. ├── splitting This folder contains numpy files which help to match compounds and features to create training and test sets for a split, which can be reused in the analysis notebook for data preparation. ├── scaffold_based_split.npz Splitting for scaffold-based splits. ├── random_split_{}.npz Random split indices of test set compounds (10 files). ├── cross_validation_indicies.npz Indices for random cross-validation splits ├── GE_clusters_size_constrained.npz Indicies of clusters of same-size clustering for gene-expression features. ├── MOBC_clusters_size_constrained.npz Indices of clusters of same-size clustering for batch-corrected morphology features.</code></pre> <p> </p>
Bioactivity deep learning for structure-free compound-protein interaction
<p>CPI2M data for "<strong>Bioactivity deep learning for structure-free compound-protein interaction</strong>".</p> <p>CPI2M_main_Ki.csv: Bioactivity data with <strong>pKi </strong>activity type. Used for model training and internal validation.</p> <p>CPI2M_main_Kd.csv: Bioactivity data with <strong>pKd</strong> activity type. Used for model training and internal validation.</p> <p>CPI2M_main_EC50.csv: Bioactivity data with <strong>pEC50 </strong>activity type. Used for model training and internal validation.</p> <p>CPI2M_main_IC50.csv: Bioactivity data with <strong>pIC50 </strong>activity type. Used for model training and internal validation.</p> <p>CPI2M_few_Ki.csv: Bioactivity data with <strong>pKi </strong>activity type. Used for external validation.</p> <p>CPI2M_few_Kd.csv: Bioactivity data with <strong>pKd </strong>activity type. Used for external validation.</p> <p>CPI2M_few_EC50.csv: Bioactivity data with <strong>pEC50 </strong>activity type. Used for external validation.</p> <p>CPI2M_few_IC50.csv: Bioactivity data with <strong>pIC50 </strong>activity type. Used for external validation.</p> <p>potency.csv: BIoactivity data with <strong>pPotency </strong>activity type. Not used currently but can be potentially adopted as classification data for customized use.</p> <p>percentage.csv: BIoactivity data with <strong>Percentage Inhibition </strong>activity type. Not used currently but can be potentially adopted as classification data for customized use.</p> <p>Protein_pretrained_feat.zip: pre-calculated protein feature files with UniProt ID naming. <strong>Should be unzipped</strong> before start model training with CPI2M data.</p> <p> </p> <p>For each .csv data, columns include "<strong>smiles</strong>" (ligand SMILES), "<strong>exp_mean</strong>" (nM bioactivity), "<strong>y</strong>" (neg.log nM, final label), "<strong>cliff_mol</strong>" (whether activity cliff or not), "<strong>split</strong>" (splitting label by activity cliff), "<strong>Uniprot_id</strong>" (UniProt ID for protein), "<strong>Sequence</strong>" (wildtype sequence for protein), and "type_id" (bioactivity type token, pKi =0, pKd=1, pEC50=2, pIC50=3).</p> <p> </p> <p>Please find the project code at https://github.com/gu-yaowen/GGAP-CPI</p> <p> </p>
Bioactive compounds with no structural analogs (high-confidence activity data)
<p>A set of 52,815 unique bioactive compounds (human targets, high-confidence activity data) with no structural analogs with high-confidence activity data was extracted from ChEMBL. For each compound the ChEMBL compound ID (CHEMBLID_Compound) and high-confidence target annotation(s) (CHEMBLID_Targets) are provided. The data set was generated as a part of an analysis to be published in 'Medicinal Chemistry Communications'. </p>
Figure 3 in Partially purified Glycine max proteinase inhibitors: potential bioactive compounds against tobacco cutworm, Spodoptera litura (Fabricius, 1775) (Lepidoptera: Noctuidae)
Figure 3. Food assimilation (in mg) with respect to control when second-instar larvae of S. litura were given different concentrations of soybean PIs. Columns and bars represent the mean ± SE. Different letters above the columns representing each concentration indicate significant differences with Tukey's test at P ≤ 0.05.
Figure 2 in Partially purified Glycine max proteinase inhibitors: potential bioactive compounds against tobacco cutworm, Spodoptera litura (Fabricius, 1775) (Lepidoptera: Noctuidae)
Figure 2. Percentage survival of adults when second-instar larvae of S. litura were given different concentrations of soybean PIs. Columns and bars represent the mean ± SE. Different letters above the columns representing each concentration indicate significant differences with Tukey's test at P ≤ 0.05.
Figure 1 in Partially purified Glycine max proteinase inhibitors: potential bioactive compounds against tobacco cutworm, Spodoptera litura (Fabricius, 1775) (Lepidoptera: Noctuidae)
Figure 1. (A) Normal S. litura adult, (B–D) abnormality in adults observed at 100 µg/mL concentration of soybean PIs.
Figure 4 in Partially purified Glycine max proteinase inhibitors: potential bioactive compounds against tobacco cutworm, Spodoptera litura (Fabricius, 1775) (Lepidoptera: Noctuidae)
Figure 4. Trypsin activity in larvae of S. litura at different time intervals under the influence of partially purified soybean PIs.
Bioactive compound classes from ChEMBL20 for molecular hierarchy
<p>Compound structure, scaffold, and CSK of 78,150 (all) and 54,042 (only compounds with a scaffold shared by at least one another compound) compounds, respectively, are provided herein. In addition, each compound is annotated with target and potency information.</p>
Bioactive compounds from Crataegus Oxycantha Protects myocardium against important proteins of atherosclerosis. An in-depth analysis using molecular simulation studies
<p>We are submitting this data as a raw file for the submission of our manuscript to the peer reviwed journals. </p>
A bioactive compound isolated from Duku (Lansium domesticum Corr) fruit peels exhibits cytotoxicity against T47D cell line
<p><strong>Background: </strong>Breast cancer is a major health problem for women globally. Many attempts have been promoted to cure cancer by finding new anticancer medicines from natural resources. Despite the richness of biodiversity discovered, there are some natural resources that remain unexplored. Fruit peels of Duku (<em>Lansium domesticum</em> Corr.) are rich with compounds that may have the potential to be developed as anticancer drugs. This study aimed to isolate cytotoxic compounds from the fruit peels of <em>L. domesticum</em> and assess their cytotoxic nature against T47D cells.</p> <p><strong>Methods: </strong>Powdered peels were macerated with ethyl acetate and the filtrate was evaporated to give EtOAc extract A. Dried extract A was triturated with n-hexane to give n-hexane soluble fraction B and insoluble fraction C. The cytotoxic nature of these three samples were assessed using MTT assay using T47D cells and doxorubicin as a control</p> <p><strong>Results: </strong>Fraction C that showed the smallest IC<sub>50</sub> (25.56 + 0.64µg/mL) value compared to extract A and fraction BFraction C was further fractionated by vacuum liquid chromatography to give 6 subfractions. Subfraction 2 showed a single compound based on thin layer chromatography, and this compound was identified as Lamesticumin A on the basis of its spectroscopic data. Lamesticumin A demonstrated cytotoxic activity against T47D cell lines with an IC<sub>50</sub> value of 15.68 + 0.30µg/mL.</p>
Reduction and modification of graphene derivatives with bioactive compounds
Open the record for dataset details and reuse information.
Data from: Long read reference genome-free reconstruction of a full-length transcriptome from Astragalus membranaceus reveals transcript variants involved in bioactive compound biosynthesis
Astragalus membranaceus, also known as Huangqi in China, is one of the most widely used medicinal herbs in Traditional Chinese Medicine. Traditional Chinese Medicine formulations from Astragalus membranaceus have been used to treat a wide range of illnesses, such as cardiovascular disease, type 2 diabetes, nephritis and cancers. Pharmacological studies have shown that immunomodulating, anti-hyperglycemic, anti-inflammatory, antioxidant and antiviral activities exist in the extract of Astragalus membranaceus. Therefore, characterising the biosynthesis of bioactive compounds in Astragalus membranaceus, such as Astragalosides, Calycosin and Calycosin-7-O-β-D-glucoside, is of particular importance for further genetic studies of Astragalus membranaceus. In this study, we reconstructed the Astragalus membranaceus full-length transcriptomes from leaf and root tissues using PacBio Iso-Seq long reads. We identified 27 975 and 22 343 full-length unique transcript models in each tissue respectively. Compared with previous studies that used short read sequencing, our reconstructed transcripts are longer, and are more likely to be full-length and include numerous transcript variants. Moreover, we also re-characterised and identified potential transcript variants of genes involved in Astragalosides, Calycosin and Calycosin-7-O-β-D-glucoside biosynthesis. In conclusion, our study provides a practical pipeline to characterise the full-length transcriptome for species without a reference genome and a useful genomic resource for exploring the biosynthesis of active compounds in Astragalus membranaceus.
Bioactive compounds, antioxidant activity, and sensory profile of sorghum tisane
<p>This dataset provides the result of bioactive compounds, antioxidant activity, sensory acceptance, and sensory profile measurements towards sorghum tisane with different pericarp colors.</p>
Fig. 4 in Bioactive prenylated phenolic compounds from the aerial parts of Glycyrrhiza uralensis
Fig. 4. The docking model and IC50 values of (1''R, 2''S)-1 and (1''S, 2''R)-1 against 3CLpro and PLpro. Each data point is displayed as the mean SD of three ± independent tests.
Fig. 3 in Bioactive prenylated phenolic compounds from the aerial parts of Glycyrrhiza uralensis
Fig. 3. The comparison of experimental and calculated ECD spectra of (1′′R, 2′′S)-1, (1′′S, 2′′R)-1, and 6.
Fig. 5 in Bioactive prenylated phenolic compounds from the aerial parts of Glycyrrhiza uralensis
Fig. 5. Bioactivity screening of compounds 1-26 [1a, (1''R, 2''S)-1; 1b, (1''S, 2''R)-1]. Each data point is displayed as the mean ± SD of three independent tests. ×: not tested.
Fig. 1 in Plant-cyanobacteria interactions: Beneficial and harmful effects of cyanobacterial bioactive compounds on soil-plant systems and subsequent risk to animal and human health
Fig. 1. Cyanobacterial active compounds induce negative, (A) ROS and enzyme activities such as superoxide dismutase (SOD), glutathione peroxidase (GPx), peroxidase (POD); and positive effects (B) expression of stress responsive genes (Ssglc and slr1562) that can have a positive effect on increasing plants' stress tolerance.
Fig. 3 in Bioactive sulfur-containing compounds from Xanthium sibiricum, including a revision of the structure of xanthiazinone
Fig. 3. Perspective drawing of the X-ray structures of 1–3, 5, and 7 (with thermal ellipsoid probability of 30%).
Effect of Supplementation of Bioactive Compounds on the Energy Metabolism of People Living With HIV / AIDS
ClinicalTrials.gov study NCT03141918. IPD Sharing: NO. Countries: 1. Publications: 3.
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.