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56 results for “virtual screening”
Supporting data: Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?
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Virtual screening for functional foods against the main protease of 2019 novel coronavirus
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Supplementary material 1 from: Ruswanto R, Mardianingrum R, Septian AD, Yanuar A (2023) The design and virtual screening of thiourea derivatives as a Sirtuin-1 inhibitor. Pharmacia 70(4): 1335-1344. https://doi.org/10.3897/pharmacia.70.e108012
The structure and docking results of the 1-benzoyl-3-methylthioureas derivatives
Improving Plasmodium N-Myristoyltransferase Inhibitors Discovery through Selective Virtual Screening
<p><span>The docked structure, MD simulation trajectories data of NMT project.</span></p>
Virtual Reality Compared to Screen Based Engagement on Mood
ClinicalTrials.gov study NCT05249582. IPD Sharing: NO. Countries: 1. Publications: 0.
Identification of a selective G1-phase benzimidazolone inhibitor by a senescence-targeted virtual screen using artificial neural networks
GEO Series GSE72621. Homo sapiens. 38 samples. Type: Expression profiling by array.
Molecular Docking and Virtual Screening of 716 Candidate Natural Bioactive Compounds Against Spike Receptor-Binding Domain of Coronavirus reveals Octahydroeuclein as possible inhibitor
<p>the data of study shows a possible drug candidate for COVID-19</p>
Beware of the generic machine learning-based scoring functions in structure-based virtual screening
<p>Data sets and the rescoing scores utilized in the paper "Beware of the generic machine learning-based scoring functions in structure-based virtual screening" (DOI: 10.1093/bib/bbaa070)</p>
Accuracy or novelty: what can we gain from target-specific machine learning-based scoring functions in virtual screening?
<p>Datasets, features, and some representative scripts utilized in the paper "Accuracy or novelty: what can we gain from target-specific machine learning-based scoring functions in virtual screening?" </p>
A Mechanism to Open Academic Chemistry to High-Throughput Virtual Screening
<h1>Pan-Canadian Chemical Library</h1> <p>This Zenodo repository contains the cheap and druglike subset of the Pan-Canadian Chemical Library (PCCL) project. For more information, visit <a href="https://pccl.thesgc.org/" rel="nofollow">https://pccl.thesgc.org</a>.</p> <h2>PCCL library</h2> <p>The PCCL library is splitted by reaction, then by number of heavy atoms. Two types of files are available in zip archives:</p> <ul> <li>The SMILES format files, with the SMILES string and their product name,</li> <li>The CSV format file, with all the information generated during their enumeration: reagents, druglike properties, etc.</li> </ul> <p>Note: Purchasability is defined according to two integers: 1 for products only composed of BB-50 reagents, and 2 for products composed of BB-40 or BB-50 reagents. Read more about the meaning of these reagents groups in the article below.</p> <h2><a href="https://github.com/cbedart/PCCL#citation"></a>Citation</h2> <p>If you find the PCCL useful or if you use it, please cite our paper:</p> <p>Bedart, C. <em>et al.</em> A Mechanism to Open Academic Chemistry to High-Throughput Virtual Screening. ChemRxiv (2023).<br>doi:10.26434/chemrxiv-2023-jgbgv<br>This content is a preprint and has not been peer-reviewed.</p>
Virtual Reality a Novel Screening and Treatment Aid in Attention Deficit Disorder
ClinicalTrials.gov study NCT00364702. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Screens, Virtual Reality and Digital Addiction (EVADD)
ClinicalTrials.gov study NCT05860660. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Feasibility, Adoption and Efficacy of A Virtual Reality Smoking Cessation Program for Patients Undergoing Lung Cancer Screening
ClinicalTrials.gov study NCT06021652. IPD Sharing: NO. Countries: 0. Publications: 0.
Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers
<p>The 12 VS scenarios considered in this study employing six training-test data partitions<strong> </strong>(A-F). All training sets employ the same set of 371 actives (WO2015160641A2), but differ on the considered set of inactives and hence are uniquely identified by the latter (either TrueInactives, DeepCoys, RandomDecoys or ActivesOnly). Likewise, all test sets employ the same 297 actives (WO201503820A1), none of them also included in the training set, but different sets of inactives (TrueInactives or DeepCoys). </p> <p> </p> <table align="center"> <caption>Table 1. Six virtual screening scenarios corresponding to six pairs of training-test data for each type of SFs (classification or regression)</caption> <thead> <tr> <th scope="col">Partition ID</th> <th scope="col">Training set</th> <th scope="col">Test set</th> <th scope="col">Type</th> </tr> </thead> <tbody> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Regression</td> </tr> </tbody> </table> <p> </p>
Identification of Potential Multi-Target Directed Ligands Through Virtual Screening and Molecular Dynamics Simulation Approach for the Treatment of Alzheimer's Disease
<p>Alzheimer’s disease (AD) is a multifactorial neurological disorder characterized by memory loss and cognitive impairment. The currently available single-targeting drugs have miserably failed in the treatment of AD and multi-target directed ligands (MTDLs) are being explored as an alternative strategy. Cholinesterase and monoamine oxidase enzymes are reported to play crucial role in the pathology of AD and multipotent ligands targeting these two enzymes simultaneously, are under various phases of design and development. Recent studies have revealed that computational approaches are robust and trusted tools for the identification of novel therapeutics. The current research work is focused on the development of potential multitarget directed ligands that simultaneously inhibit acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B) enzymes employing structure-based virtual screening (SBDD) approach. The ASINEX database was screened after applying pan assay interference and drug likeness filter to identify novel molecules using three docking precision criteria Highthroughput virtual screening (HTVS), Standard Precision (SP), and extra precision (XP). Additionally, binding free energy calculations, ADME and molecular dynamic simulations were also employed to get structural insights into mechanism of protein-ligand binding and pharmacokinetic properties. Three lead molecules viz. AOP19078710, BAS00314308 and BDD26909696 were successfully identified which displayed binding score of -10.565, -10.543 & -8.066 kcal/mol against AChE and -11.019, -12.357 & -10.068 kcal/mol against MAO-B, better score as compared to the standard inhibitors. In near future, these molecules will be synthesized and evaluated through in vitro and in vivo assays for their inhibition potential against AChE and MAO-B enzymes.</p>
Identification of Potential DNA Gyrase Inhibitors: Virtual Screening, Extra-Precision Docking and Molecular Dynamics Simulation Study
<p>These are the data generated during the research. All these data will be linked to the entitled work, which will be published as a research article.</p>
ScienceDex guides
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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.