ExploreResults of default run of Schrödinger QikProp (Schrödinger, LLC, New York, NY, Schrödinger Release 2015-4, 2015, QikProp v4.6.) for the entire in silico pteridine library. QikProp in silico predicts various physico-chemical compound properties and advanced descriptors related to administration, distribution, metabolism, excretion and toxicity of the compounds.
SEEK ID: https://fairdomhub.org/data_files/3838?version=1
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qikprop-results_insilicolibrary-pteridines.csv
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Created: 22nd Sep 2020 at 14:10
Last updated: 30th Sep 2020 at 12:03
Last used: 22nd May 2022 at 14:25
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Version 1 Created 22nd Sep 2020 at 14:10 by Ina Poehner
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Projects: Kinetics on the move - Workshop 2016, COVID-19 Disease Map, NMTrypI - New Medicines for Trypanosomatidic Infections, CoVIDD - Coronavirus interactions in drug discovery - optimization and implementation
Institutions: Kinetics on the move Workshop at HITS, Heidelberg Institute for Theoretical Studies (HITS gGmbH), University of Eastern Finland (UEF)
https://orcid.org/0000-0002-2801-8902
Group Leader, Molecular and Cellular Modeling Group, EML Research, Heidelberg
Projects: NMTrypI - New Medicines for Trypanosomatidic Infections
Web page: https://cordis.europa.eu/programme/id/FP7_HEALTH.2013.2.3.4-2
The New Medicines for Trypanosomatidic Infections - NMTrypI project aimed at obtaining new candidate drugs against Trypanosomatidic infections with appropriate efficiency from the lead phase to the final preclinical phase that are more accessible to patients.
Programme: Drug development for neglected parasitic diseases
Public web page: https://fp7-nmtrypi.eu/
Start date: 1st Feb 2014
End date: 31st Jan 2017
Multidisciplinary development of selective anti-parasitic multi-target inhibitors of PTR1/DHFR based on a pteridine scaffold.
Submitter: Ina Poehner
Studies: Docking to PTR1 and DHFR targets and off-targets, In silico property and correlation analysis
Assays: Compound library preparation, Correlation analysis between PTR1 and DHFR activities and anti-parasitic..., Correlation analysis between predicted ADMET properties and anti-parasit..., Docking receptor preparation, In silico ADMET data prediction, Induced-fit docking studies, PAINS filtering, Rigid-body docking studies
Computational prediction of physicochemical and advanced descriptors related to ADME-Tox and PAINS assessment. Potential correlations of the computed descriptors with experimentally determined anti-parasitic activities and correlations between experimentally determined levels of target protein inhibition (PTR1, DHFR) with the anti-parasitic activity were also studied.
Computational prediction of physicochemical and advanced descriptors related to ADME-Tox.
Submitter: Ina Poehner
Biological problem addressed: Model Analysis Type
Investigation: Pteridines as anti-kinetoplastid folate-pathway...
