Python module for calculating pairwise correlation coefficients for Qikprop descriptors and measured activity data Version 1
Zip file with Python module used to correlate compound descriptors predicted with Schroedinger QikProp with observed anti-parasitic effect against T. brucei brucei bloodstream forms. The script allows a leave-one-out analysis in addition to the default correlation analysis, where data for each compound is skipped once before the correlation analysis is re-performed. An example config file is provided with the zip-archive. For further instructions and information on the available settings, also refer to the README file.
SEEK ID: https://fairdomhub.org/data_files/3859?version=1
Filename:
propertyActivityCorrelations.zip
Format: Zip file
Size: 10.1 KB
Export PNG
Creators and Submitter
Views: 1715 Downloads: 77
Created: 29th Sep 2020 at 19:07
Last updated: 30th Sep 2020 at 10:03
AttributionsNone
Version History
Version 1 (earliest) Created 29th Sep 2020 at 19:07 by Ina Poehner
No revision comments
Related items
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.
Assessment of possible linear correlations between predicted ADMET descriptors from QikProp (Schrödinger, LLC, New York, NY) runs and experimentally determined activities against T. brucei brucei bloodstream forms with the help of a Python script.
Submitter: Ina Poehner
Biological problem addressed: Model Analysis Type
Investigation: Pteridines as anti-kinetoplastid folate-pathway...
