In silico ADMET data prediction
Computational prediction of physicochemical and advanced descriptors related to ADME-Tox.
SEEK ID: https://fairdomhub.org/assays/1332
Modelling analysis
Projects: NMTrypI - New Medicines for Trypanosomatidic Infections
Investigation: Pteridines as anti-kinetoplastid folate-pathway protein inhibitors
Study: In silico property and correlation analysis
Assay position:
Biological problem addressed: Model Analysis Type
Organisms: No organisms
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Created: 19th Aug 2020 at 11:25
Last updated: 30th Sep 2020 at 10:03
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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.
Results of default run of Schrödinger QikProp (Schrödinger, LLC, New York, NY, Schrödinger Release 2015-4, 2015, QikProp v4.6.) for reference pteridines (series 1) and newly synthesized N10-, PABA and tail-modified pteridines (series 2, 3 and 4). QikProp in silico predicts various physico-chemical compound properties and advanced descriptors related to administration, distribution, metabolism, excretion and toxicity of the compounds.
Results of default run of Schrödinger QikProp (Schrödinger, LLC, New York, NY, Schrödinger Release 2015-4, 2015, QikProp v4.6.) for newly synthesized compounds of the merged series (5a-5f). QikProp in silico predicts various physico-chemical compound properties and advanced descriptors related to administration, distribution, metabolism, excretion and toxicity of the compounds.
Results 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.
This SOP describes the in silico ADME-Tox property prediction with QikProp for prepared pteridine ligands.
Creators: Ina Poehner, Rebecca Wade
Submitter: Ina Poehner
