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opencc-by-4.0Aug 2020View details →

Non-target screening of organic compounds in offshore produced water by GC×GC-MS (associated data)

Associated data for the manuscript titled "Non-target screening of organic compounds in offshore produced water by GC×GC-MS" Preprint doi://10.26434/chemrxiv.13317938

opencc-by-4.0Nov 2024View details →

Machine learning and multi-layer molecular network-assisted screening uncovers unknown compounds in the fentanyl family

These LC-HRMS data was collected in study of Fentanyl-Hunte. All source codes along with a user manual are available for scientific research purposes at https://github.com/FangLabNTU/Fentanyl-Hunter.

opencc-by-4.0Jun 2022View details →

Virtual screening on Nsp16: screening of 1084 compounds in VeroE6-eGFP cells

This report describes the most relevant results of virtually screening the Janssen Pharmaceutica compound collection for potential activity against SARS-CoV-2 Nsp16 and confirmation of potential hits in a VeroE6 cell-based anti-SARS-CoV-2 assay.

opencc-by-4.0Nov 2017View details →

Analog Series of Compounds with High Frequency of Activity in Screening Assays

A set of 6941 analog series and associated data are provided. These series exclusively consist of compounds that are most frequently active across public screening assays.

opencc-by-4.0May 2024View details →

Datasets of DFT adsorption energies of H and for O and OH on different pure metals and binary intermetallic compounds considering the application of elastic strains and lists of candidates for screening

This resource contains two datasets and two lists of candidates for screening in JSON format. Also It contains ZIP folders with all Quantum Espresso Inputs and outputs from which the JSON datasets were obtained. All Quantum Espresso outputs will be later added to Catalysis Hub (https://www.catalysis-hub.org/). The file "QuantumEspresso_versions" is a text file contaning the information of the Quantum Espresso versions employed for obtaining the dataset. The datasets contain the adsorption energies for surface slabs of a large number of binary intermetallic compounds with different compositions and lattices (for instance, A3B fcc, A3B hpc, AB bcc, etc.). Adsorption energies were computed for different adsorbates (H, O, and OH) on distinct adsorption sites (e.g., fcc AAB, fcc AAA, hcp AAA, hcp AAB, on-top A, and on-top B) and minimum energy surfaces. In addition, different elastic strains (biaxial tension, biaxial compression) were applied to assess their effect on adsorption energies. All calculations were carried out using DFT approximations as implemented in the Open-source software Quantum Espresso. Besides the adsorption energies, the datasets also contain relevant geometric and electronic descriptors (PSI, cell volume, weighted atomic radius, generalized coordination number, weighted electronegativity, weighted first ionization energy, outer electrons, and biaxial strain)  calculated to feed them as features in the training of ML models. The datasets with the tag "scaled" on its name have the descriptors scaled following a MinMax scaling and are given in xlsx format. The lists for screening contain candidates not included in the dataset for which Random Forest predictions of the Eads were obtained. The lists contain the geometric and electronic descriptors of all screening candidates, as well as the predicted adsorption energy (Eads_RF). A GitHub repository is linked to this dataset (https://github.com/vvassilevg/HighHydrogenML). The repository contains two Python scripts: 1) Script for creating a dataset from QuantumEspresso outputs, where all relevant descriptors are computed. It outputs a pickle and json files that can be later converted to any other desired format (like xlsx). 2) Script for training a Random Forest model for the prediction of adsorption energies (the datasets with the "scaled" tag must be used for the script to work correctly).   The dataset, ML model and screening have been accepted for publication in Catalysis Science & Technology DOI: DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D</a>. The accepted Manuscript and the Supplementary information are avilable within this repository.   If you use this dataset or any of the files within this repository, please cite the original publication (<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D)</a> in your work.

opencc-by-4.0Apr 2018View details →

Compound profiling matrices extracted from screening data

Compound profiling matrices record assay results for compound libraries tested against panels of targets. In addition to their relevance for exploring structure-activity relationships, such matrices are of considerable interest for chemoinformatic and chemogenomic applications. For example, profiling matrices provide a valuable data resource for the development and evaluation of machine learning approaches for multi-task activity prediction. However, experimental compound profiling matrices are rare in the public domain. Although they are generated in pharmaceutical settings, they are typically not disclosed. Herein, we present an algorithm for the generation of large profiling matrices, for example, containing more than 100,000 compounds exhaustively tested against 50 to 100 targets. The new methodology is a variant of bi-clustering algorithms originally introduced for large-scale analysis of genomics data. Our approach is applied here to assays from the PubChem BioAssay database and generates profiling matrices of increasing assay or compound coverage by iterative removal of entities that limit coverage. Weight settings control final matrix size by preferentially retaining assays or compounds. In addition, the methodology can also be applied to generate matrices enriched with active entries representing above-average assay hit rates.

opencc-by-4.0Jul 2021View details →

BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space

BonMOLière is a set of small-sized libraries of readily purchasable compounds, optimized to produce genuine hits in biological screens across the protein space. The corresponding research article has been published in Int. J. Mol. Sci. 2021, 22(15), 7773, DOI: <a href="https://doi.org/10.3390/ijms22157773">https://doi.org/10.3390/ijms22157773</a>

opencc-zeroSep 2022View details →

Reduction of chickens use to perform in vitro pre-screening of novel anticoccidials by miniaturisation and increased throughput of the current Eimeria tenella compound-screening model

In vitro models have supported important advances in biomedical sciences and have significantly contributed to reduce the use of experimental animals in different disciplines. We have developed an in vitro model for the evaluation of potential anticoccidial properties of novel compounds aimed to control chicken coccidiosis, a costly disease for the poultry industry. This disease is caused by protozoan parasites of the genus Eimeria (Apicomplexa), and it is mainly controlled by chemoprophylaxis with ionophors and chemical anticoccidials; however, there is an overall agreement about the limitation of these classical drugs and the need to improve current methods of control. Anticoccidial activities of novel compounds is currently evaluated by expensive experiments that involve large numbers of chickens. The use of our in vitro model for the pre-screening of essential oils led to a reduction of 67% of the chickens used in the vivo trials for validation. In this study, we describe how further optimisation of this in vitro model by miniaturisation can have an additional impact on the number of chickens used for the generation of parasite stocks for provision of the in vitro model (which cannot be done in vitro). We have estimated that the use of one chicken could support the evaluation of ten compounds with a 96-well plate format vs. only two with a 24-well plate format, which means an 80% of chicken use reduction. In this study, we have proved that the miniaturisation into a 96-well plate format has perfectly mimicked the invasion and replication observed before in the 24-well plate format. In addition, this format has allowed the simultaneous pre-screening of higher numbers of anticoccidial drugs at different concentrations following streamlined protocols in a more cost-effective way, factors that are beneficial for a wider uptake of the model by other researchers investigating anticoccidial compounds.

opencc-by-4.0Dec 2021View details →

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

opencc-by-4.0Jun 2018View details →

Compounds of interest identified by screening a focused library against USP5 Zf-UBD with a 19F NMR assay

Screening a set of commercial compounds selected from computational docking studies against USP5 zinc finger ubiquitin binding domain (Zf-UBD) using 19F NMR spectroscopy. You can find details of preliminary 19F NMR experiments <a href="https://zenodo.org/record/1246807#.WxV8HkgvzIV">here</a>.

opencc-by-4.0Jul 2018View details →

Screening compounds of interest against USP5 Zf-UBD with a surface plasmon resonance assay

The determination of binding affinities of compounds of interest against USP5 zinc finger ubiquitin binding domain (Zf-UBD) with a surface plasmon resonance (SPR) assay.

opencc-by-4.0Sep 2018View details →

Screening Compounds against USP5 Zf-UBD with a 19F NMR assay #2

Screening compounds against USP5 zinc finger ubiquitin binding domain (Zf-UBD) using a 19F NMR spectroscopy. You can find previous 19F NMR experiments <a href="https://openlabnotebooks.org/co-crystal-structures-of-usp5-zf-ubd-and-weak-binding-compounds/">here</a>.

opencc-by-4.0Oct 2018View details →

Screening Compounds of Interest against USP5 Zf-UBD by SPR #2

Determination of binding affinities of compounds of interest previously identified in a 19F-NMR spectroscopy assay against USP5 zinc finger ubiquitin binding domain (Zf-UBD) with a surface plasmon resonance (SPR) assay

opencc-by-4.0Aug 2024View details →

Primary in vivo screen for MAPK/ERK pathway inhibitors among 433 annotated oncology related compounds

The aim was to test if a C. elegans model could identify known MEK inhibitors in a blinded screen of 433 annotated oncology related compounds. Primary hits were further validated for pathway specificity. Please see the paper cross-reference for more details. We bought a chemical library (SciLife lab, Stockholm, Sweden) with 433 oncology-related compounds (see table S1 for the full list), where compounds had been acoustically dispensed from 10 mM stocks into 96-well plates. DMSO concentrations were adjusted to 0.5 % in all wells, including negative controls. Trametinib was used as a positive control at 7 muM. All chemicals were kept at -20 degrees C until use. The C. elegans strain ST65 was used. We dispensed 50 muL of worm culture into each well and incubated animals for 4-5 days at 20 degrees C until DMSO control animals reached early adulthood. Animals were then sedated for 30 minutes by adding 4 muL 20 mM levamisole per well before scoring. Primary hit selection: 1) Wells were excluded if they contained progeny or fluorescent specles from drugs or bacteria, indicated by too many objects classified as vulvae. The cutoff was set to vulvae >2x the number of adults in the positive control. 2) Wells were excluded they contained < 30 % adults compared to the positive control, indicative of severely reduced larval growth. 3) Remaining compounds with vulvae/adult scores < 0.4 were considered primary hits. Scoring of the Muv phenotype in adult lin-1(e1777) mutants was done manually from ImageXpress images. See the paper cross-reference for more details.

publicSep 2022View details →

Reduction of chickens use to perform in vitro pre-screening of novel anticoccidials by miniaturisation and increased throughput of the current Eimeria tenella compound-screening model

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publicSep 2025View details →

Data from: Epigenetic small molecule screening identifies a new HDACi compound for ameliorating Duchenne muscular dystrophy

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