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226 results for “Active compounds”
Compound activity data sets for 15 biological targets compiled from the ChEMBL and PubChem databases.
<p>Compound activity data sets for the 15 biological targets are deposited, along with structure-activity relationship matrices IDs. Active compounds were extracted from the ChEMBL database and inactive were from the PubChem database. Details of the data sets are described in the original publication. and the summary of the data sets is given in the readme.txt file. </p>
Compounds with multi-target activity from X-ray structures, corresponding analog series, and associated scaffolds
<p>A total of 702 crystallographic ligands with activity against multiple targets from different protein families (multi-family ligands) were extracted from the Protein Data Bank (PDB). These ligands are made available as aromatic non-stereo SMILES strings together with their target information. A subset of these ligands were also found in the ChEMBL database yielding additional target annotations. Target and family assignments were made following the UniProt classification scheme. In addition, 133 analog-series-based (ASB) scaffolds were derived from series of PDB ligands and structural analogs identified in ChEMBL, which are also made available. For each ASB scaffold, the number of analogs, targets, and target families is provided (union of PDB and ChEMBL annotations).</p>
Fig. 1 Screening flow, testing 1600 FDA compounds against S in Assessment of FDA-approved drugs against StrongyloideS rAtti in vitro and in vivo to identify potentially active drugs against strongyloidiasis
Fig. 1 Screening flow, testing 1600 FDA compounds against S. ratti
Figure 1 in Quality of cosmetics with active caffeine in cream and gel galenic bases prepared by compounding pharmacies
Figure 1. pH analysis for cosmetics in cream and gel base.
Figure 2 in Quality of cosmetics with active caffeine in cream and gel galenic bases prepared by compounding pharmacies
Figure 2. Color analysis for cosmetics in cream and gel base.
Figure 5 in Quality of cosmetics with active caffeine in cream and gel galenic bases prepared by compounding pharmacies
Figure 5. Wavelength analysis for cosmetics in cream and gel base.
Pyridinylimidazoles as GSK3β inhibitors: the impact of tautomerism on compound activity via water networks
<p>Data related to publication:</p> <p>Heider et al.: Pyridinylimidazoles as GSK3β inhibitors: the impact of tautomerism on compound activity via water networks.</p> <p>The files include:</p> <p>1) Output conformations of the QM Tautomer & Conformation Predictor of Maestro (Schrödinger, LLC, New York, NY, 2018) of the compounds <strong>1</strong>, <strong>3a</strong>, <strong>3b</strong>, <strong>3c</strong>, <strong>3d</strong>, <strong>3e</strong>, <strong>3f</strong>, <strong>3g</strong>, <strong>3h</strong>, <strong>3j</strong>, <strong>3k</strong>, <strong>3l</strong>, <strong>3m</strong>, <strong>6f</strong> and <strong>6g</strong> [.mae and .sdf files].</p> <p>2) Movies of the MD simulations of compounds <strong>3a</strong>, <strong>3j</strong>, <strong>4a</strong>, <strong>4b</strong>, <strong>6b</strong>, <strong>6g</strong> (6GN1) and movies (*_2) of <strong>3a</strong>, <strong>3j</strong>, <strong>6b</strong>, <strong>6g</strong> (4PTC) [.mpg files] .</p> <p>3) Raw Desmond trajectory files of the MD simulations of the compounds <strong>3a</strong>, <strong>3j</strong>, <strong>4a</strong>, <strong>4b</strong>, <strong>6b</strong>, <strong>6g</strong> [out.cms and the full trj files].</p> <p> </p> <p><br> </p>
Compound activity classes from ChEMBL for machine learning analysis
<p>Ten activity classes are provided that were extracted from ChEMBL version 24 for machine learning studies. Compounds are given in SMILES representations. The following selection criteria were applied. Compounds were required to be tested in a direct binding assay against a single human protein with a ChEMBL assay confidence score of 9. In addition, K<sub>i</sub> measurements had to be available. If multiple K<sub>i</sub> values were available for a compound and did not fall within the same order of magnitude, the compound was not selected. Furthermore only compounds with (mean) pK<sub>i</sub> of at least 5 were considered. Moreover, activity classes had to contain at least 200 compounds belonging to at least 50 computationally determined analog series. The 10 deposited classes consist of 243 to 955 compounds and 57 to 216 analog series.</p>
Promiscuous compounds with activity against different target classes
<p>Reported are 1063 promiscuous compounds with activity against multiple target classes identified in PubChem. The tab-separated file consists of four columns:</p> <p>- cid: compound identifier (CID) from PubChem</p> <p>- NonstereoAromaticSMILES: SMILES representation of a compound</p> <p>- Number_of_tested_targets: total number of targets a compound was tested against in PubChem</p> <p>- PD: promiscuity degree (PD) of a compound, defined as the number of targets it was found active against</p>
Can metal organic frameworks outperform adsorptive removal of harmful phenolic compound 2-chlorophenol by activated carbon?
<p>A more complete version of this dataset, along with publication details, is available from <a href="https://zenodo.org/record/2586955">https://zenodo.org/record/2586955</a></p>
Classification of Binding Modes for Kinase-Inhibitor Complex Structures, 3D Activity Cliffs Formed by Kinase Inhibitors, and Structural Analogues of 3D-Cliff Compounds
<p>The classification of crystallographic binding modes is provided for 884 kinase-inhibitor complex structures that were assembled from PDB. In addition, a total of 105 three-dimensional activity cliffs formed by 3D kinase inhibitors are listed. Their corresponding potency information is also given. Furthermore, the 2D structural analogues of 3D cliff-forming inhibitors were identified from ChEMBL database, on the basis of matched molecular pairs. These analogs and their activity information are also provided.</p>
Molecular dynamics results of the complex 3CLpro active site with compound 5
<p>Molecular dynamics results of the complex 3CLpro active site with compound <strong>5</strong>. The protein structure is shown in gray and the compound <strong>5</strong> is shown in orange.</p>
Data from: Molecular docking and dynamics studies to identify novel active compounds targeting potential breast cancer receptor proteins from an indigenous herb Euphorbia thymifolia Linn
<p>Breast cancer has become most prevalent disease and their incidence has doubled in Indian scenario. Targeted therapy with the novel compounds derived from plants could be the promising approach for the development of drugs. <em>Euphorbia thymifolia</em> L is a widely growing tropical herb which has been reported for its various ethnopharmacological properties, including anticancer properties. The aim of the present study was to identify the active phytocompounds present in the methanolic extract using an <em>I</em><em>n-silico</em> approach. The methanolic extract of <em>E. thymifolia</em> (ME.ET) was subjected to GC-MS analysis and the identified compounds were docked with potential protein targets implicated in breast cancer such as ERK1, AKT, EGFR/HER2, ER, MELK, PLK1, PTK6. Compounds with good docking score were further subjected to dynamics study to understand the protein ligand binding stability, ligand pathway calculation, molecular mechanics energies combined with Poisson-Boltzmann (MM/PBSA) calculation using Schrodinger suite. Out of 219 unique phytocompounds subjected to docking, two compounds namely, 3,6,9,12-tetraoxatetradecane-1,14-diyl dibenzoate (TTDB) and succinic acid, 2-(dimethylamino)ethyl 4-isopropylphenyl ester (SADPE) showed good docking score. Molecular dynamics study showed high affinity and low binding energy for TTDB with HER2, ERK1 and SADPE with ER. Hence this is the first study to identify and report active compounds from <em>E.thymifolia</em> linn. Further <em>invitro</em> and <em>invivo</em> anticancer studies can be performed to confirm these results and understand the molecular mechanism by which TTDB and SADPE exhibit anticancer activity against breast cancer.</p>
Screening of 6000 compounds for uncoupling activity: input parameters, the predicted uncoupling activity, as well as the results in respect to structural alerts
<p>Protonophoric uncoupling of phosphorylation is an important factor when assessing chemicals for their toxicity, and has recently moved into focus in pharmaceutical research with respect to the treatment of diseases such as cancer, diabetes or obesity. Reliably identifying uncoupling activity is thus a valuable goal. To that end, we screened more than 6000 anionic compounds for in-vitro uncoupling activity, using a biophysical model based on ab-initio COSMO-RS input parameters with the molecular structure as the only external input. We combined these results with a model for baseline toxicity (narcosis). Our model identified more than 1250 possible uncouplers in the screening dataset, and identified possible new uncoupler classes such as thiophosphoric acids. When tested against 423 known uncouplers and 612 known inactive compounds in the dataset, the model reached a sensitivity of 83% and a specificity of 96%. In a direct comparison, it showed a similar specificity than the structural alert profiler Mitotox (97%), but much higher sensitivity than Mitotox (47%). The biophysical model thus allows for a more accurate screening for uncoupling activity than existing structural alert profilers. We propose to use our model as a complementary tool to screen large datasets for protonophoric uncoupling activity in drug development and toxicity assessment.</p>
Screening of 67 compounds with predicted biosignature profile similar to remdesivir for anti-SARS-CoV-2 activity
<p>This short report describes the most relevant results of screening compounds with a similar predicted biosignature as remdesivir in a VeroE6 cell-based anti-SARS-CoV-2 assay.</p>
Screening of ~7000 coronavirus-specific compounds for anti-SARS-CoV-2 activity
<p>This short report describes the most relevant results of screening selected compounds with potential activity against SARS-CoV-2 in a VeroE6 cell-based anti-SARS-CoV-2 assay.</p>
Data from: Design of cinnamaldehyde amino acid Schiff base compounds based on the quantitative structure–activity relationship
Cinnamaldehyde amino acid Schiff base (CAAS) is a new class of safe, bioactive compounds which could be developed as potential antifungal agents for fungal infections. To design new cinnamaldehyde amino acid Schiff base compounds with high bioactivity, the quantitative structure–activity relationships (QSARs) for CAAS compounds against Aspergillus niger (A. niger) and Penicillium citrinum (P. citrinum) were analysed. The QSAR models (R2 = 0.9346 for A. niger, R2 = 0.9590 for P. citrinum,) were constructed and validated. The models indicated that the molecular polarity and the Max atomic orbital electronic population had a significant effect on antifungal activity. Based on the best QSAR models, two new compounds were designed and synthesized. Antifungal activity tests proved that both of them have great bioactivity against the selected fungi.
Effects of pH and light exposure on the survival of bacteria and their ability to biodegrade organic compounds in clouds: Implications for microbial activity in acidic cloud water
<p>Our manuscript was submitted to ACP. Upon submission, all data must be accessible by anonymous access.</p>
IR data for the compounds published in "Dioxygen Activation by a Bioinspired Tungsten(IV) Complex"
Open the record for dataset details and reuse information.
IR data of the compounds published in "Replacement of Molybdenum by Tungsten in a Biomimetic Complex Leads to an Increase in Oxygen Atom Transfer Catalytic Activity"
Open the record for dataset details and reuse information.
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International Brain Laboratory public data
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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.