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14 results for “ADMET”
ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries – Data and Models
<p>This repository contains data and models used in the following paper.</p> <p> </p> <p>Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J. C., Shivnaraine, R. V., & Zou, J. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries. In review.</p> <p> </p> <p>The data and models are meant to be used with the <a href="https://github.com/swansonk14/admet_ai">ADMET-AI</a> code, which runs the ADMET-AI web server at <a href="https://admet.ai.greenstonebio.com/">admet.ai.greenstonebio.com</a>.</p> <p> </p> <p>The data.zip file has the following structure.</p> <p>data</p> <p> drugbank: Contains files with drugs from the <a href="https://go.drugbank.com/">DrugBank</a> that have received regulatory approval. drugbank_approved.csv contains the full set of approved drugs along with ADMET-AI predictions, while the other files contain subsets of these molecules used for testing the speed of ADMET prediction tools.</p> <p> tdc_admet_all: Contains the data (.csv files) and RDKit features (.npz files) for all 41 single-task ADMET datasets from the <a href="https://tdcommons.ai/">Therapeutics Data Commons</a> (TDC).</p> <p> tdc_admet_multitask: Contains the data (.csv files) and RDKit features (.npz files) for the two multi-task datasets (one regression and one classification) constructed by combining the tdc_admet_all datasets.</p> <p> tdc_admet_all.csv: A CSV file containing all 41 ADMET datasets from tdc_admet_all. This can be used to easily look up all ADMET properties for a given molecule in the TDC.</p> <p> tdc_admet_group: Contains the data (.csv files) and RDKit features (.npz files) for the 22 TDC ADMET Benchmark Group datasets with five splits per dataset.</p> <p> tdc_admet_group_raw: Contains the raw data (.csv files) used to construct the five splits per dataset in tdc_admet_group.</p> <p> </p> <p>The models.zip file has the following structure. Note that the ADMET-AI website and Python package use the multi-task Chemprop-RDKit models below.</p> <p>models</p> <p> tdc_admet_all: Contains Chemprop and Chemprop-RDKit models trained on all 41 single-task TDC ADMET datasets.</p> <p> tdc_admet_all_multitask: Contains Chemprop and Chemprop-RDKit models trained on the two multi-task TDC ADMET datasets (one regression and one classification).</p> <p> tdc_admet_group: Contains Chemprop and Chemprop-RDKit models trained on the 22 TDC ADMET Benchmark Group datasets.</p>
Apathy in Dementia Methylphenidate Trial (ADMET)
ClinicalTrials.gov study NCT01117181. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Data from: Synthesis, antimicrobial evaluation, ADMET prediction, molecular docking and dynamics studies of pyridine and thiophene moieties containing chalcones
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Supplementary Data for "Molecular Docking, ADMET, Synthesis and Evaluation of New Indomethacin Hydrazide Derivatives as Antibacterial Agents"
<p>This supplementary data file contains additional information supporting the findings presented in the research article "<strong>Molecular Docking, ADMET, Synthesis and Evaluation of New Indomethacin Hydrazide Derivatives as Antibacterial Agents</strong>". The data includes:</p> <ul> <li>Infrared (IR) spectra of the synthesized compounds</li> <li>Proton nuclear magnetic resonance (H NMR) spectra of the synthesized compounds</li> <li>Minimum inhibitory concentration (MIC) and zone of inhibition (ZOI) data for the synthesized compounds against tested microorganisms</li> </ul>
FIGURE 2 in Redescription of Admete sadko Gorbunov, 1946 (Gastropoda: Cancellariidae)
FIGURE 2. Shells of Arctic Cancellariidae 1/61903. A, B—Admete sadko: A—Kara Sea, 75°45'N, 86°05'E, 29 m, ZIN 3/53455; B— Laptev Sea, 76°09'N, 120°00'E, 64 m, ZIN 2/62429; C, D—Admete viridula: C—neotype of Tritonium viridulum, NHMD 90818, D—Barents Sea, Kola Inlet, 17 m, 69°02.26'N, 33°02.54'E, 14.08.2006; E—lectotype of Admete contabulata, ZMB 20724; Flectotype of Trichotropis inflata (ZMB 86413); G—Admete clivicola, Barents Sea, Murman Coast, 69°30.00'N, 33°30'E, 255 m, ZIN 1/62137. Scale bar = 5 mm.
Boesenbergia pandurata as Anti-breast Cancer: Molecular Docking and ADMET Study
<p><em>Boesenbergia pandurata</em> or fingerroot is known to have various pharmacological activities, including anticancer. Extracts from these plants are known to inhibit the growth of cancer cells, including breast cancer. Anti-breast cancer activity is significantly influenced by the inhibition of two receptors: ER-α and HER2. However, it is unknown which metabolites of <em>B. pandurata</em> play the most crucial role in their anticancer activity. This study aimed to determine the metabolites of <em>B. pandurata</em> with the best potential as ER-α and HER2 inhibitors. The method used was molecular docking of several <em>B. pandurata</em> metabolites against ER-α and HER2 receptors, followed by an ADMET study of several metabolites with the best docking results. The docking results showed eight metabolites with the best docking results for the two receptors based on the docking score and ligand-receptor interactions. Of these eight compounds, compounds <strong>11</strong> ((2S)-7,8-dihydro-5-hydroxy-2-methyl-2-(4''-methyl-3''-pentenyl)-8-phenyl-2H,6H-benzo(1,2-b-5,4-b')dipyran-6-one) and <strong>34</strong> (geranyl-2,4-dihydroxy-6-phenethylbenzoate) showed the potential to inhibit both receptors. Both ADMET profiles also show mixed results but still allow for further development. In conclusion, the metabolites of <em>B. pandurata</em> especially compounds <strong>11</strong> and <strong>34</strong>, can be developed as anti-breast cancer through the inhibition of ER-α and HER2.</p>
Figure 4 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967
Figure 4 Number of HBs and RMSD between N1-H274Y-oseltamivir protein (PDB ID: 3CL0) with three flavones 428, 581, and 864 (Fig. A–C, respectively) during dynamic simulation time 500 ns.
Figure 5 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967
Figure 5 The proposed semi-synthesis of compound 864. (i) Column chromatography (70% EtOH: 18.64 mg.g−1) (ii) ClCH2COCl, K2CO3, DCM, rt, 8–24h; (iii) K2CO3/KI, acetone, reflux, 25–30 h.
Figure 2 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967
Figure 2 2D interaction diagrams and 3D molecular docking of N1-H274Y-oseltamivir protein (PDB ID: 3CL0) with four flavones 428, 581, 864, and 948 (A–D, respectively) and two flavonols 162 and 218, (E, F, respectively).
Figure 3 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967
Figure 3 3D molecular docking of bifunctional N inhibitor (compound 864) binding into the sialic acid binding cavity and the 430-cavity through the unique arginine triad Arg118-Arg292-Arg371 of N1-H274Y-oseltamivir protein (PDB ID: 3CL0).
Figure 1 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967
Figure 1 Procedure of molecular docking screening and molecular dynamics of flavones and flavonols from COCONUT database.
Supplementary material 1 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967
Supporting data
FIGURE 1 in Redescription of Admete sadko Gorbunov, 1946 (Gastropoda: Cancellariidae)
FIGURE 1. Syntype of Admete sadko ZIN 1/61903. Scale bar = 5 mm.
Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.