Literature DB >> 16170050

Experimental screening of dihydrofolate reductase yields a "test set" of 50,000 small molecules for a computational data-mining and docking competition.

Nadine H Elowe1, Jan E Blanchard, Jonathan D Cechetto, Eric D Brown.   

Abstract

High-throughput screening (HTS) generates an abundance of data that are a valuable resource to be mined. Dockers and data miners can use "real-world" HTS data to test and further develop their tools. A screen of 50,000 diverse small molecules was carried out against Escherichia coli dihydrofolate reductase (DHFR) and compared with a previous screen of 50,000 compounds against the same target. Identical assays and conditions were maintained for both studies. Prior to the completion of the second screen, the original screening data were publicly released for use as a "training set", and computational chemists and data analysts were challenged to predict the activity of compounds in this second "test set". Upon completion, the primary screen of the test set generated no potent inhibitors of DHFR activity.

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Year:  2005        PMID: 16170050     DOI: 10.1177/1087057105281173

Source DB:  PubMed          Journal:  J Biomol Screen        ISSN: 1087-0571


  9 in total

1.  Here be dragons: docking and screening in an uncharted region of chemical space.

Authors:  Ruth Brenk; John J Irwin; Brian K Shoichet
Journal:  J Biomol Screen       Date:  2005-09-16

2.  Rescoring docking hit lists for model cavity sites: predictions and experimental testing.

Authors:  Alan P Graves; Devleena M Shivakumar; Sarah E Boyce; Matthew P Jacobson; David A Case; Brian K Shoichet
Journal:  J Mol Biol       Date:  2008-01-30       Impact factor: 5.469

3.  Evaluation of a Conformationally Constrained Indole Carboxamide as a Potential Efflux Pump Inhibitor in Pseudomonas aeruginosa.

Authors:  Yongzheng Zhang; Jesus D Rosado-Lugo; Pratik Datta; Yangsheng Sun; Yanlu Cao; Anamika Banerjee; Yi Yuan; Ajit K Parhi
Journal:  Antibiotics (Basel)       Date:  2022-05-26

4.  Analysis of High-Dimensional Structure-Activity Screening Datasets Using the Optimal Bit String Tree.

Authors:  Ke Zhang; Jacqueline M Hughes-Oliver; S Stanley Young
Journal:  Technometrics       Date:  2013

5.  Systematic error detection in experimental high-throughput screening.

Authors:  Plamen Dragiev; Robert Nadon; Vladimir Makarenkov
Journal:  BMC Bioinformatics       Date:  2011-01-19       Impact factor: 3.169

6.  Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies.

Authors:  Bogdan Mazoure; Robert Nadon; Vladimir Makarenkov
Journal:  Sci Rep       Date:  2017-09-20       Impact factor: 4.379

Review 7.  Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening.

Authors:  Shardul Paricharak; Oscar Méndez-Lucio; Aakash Chavan Ravindranath; Andreas Bender; Adriaan P IJzerman; Gerard J P van Westen
Journal:  Brief Bioinform       Date:  2018-03-01       Impact factor: 11.622

Review 8.  Virtual ligand screening: strategies, perspectives and limitations.

Authors:  Gerhard Klebe
Journal:  Drug Discov Today       Date:  2006-07       Impact factor: 7.851

9.  Virtual Screening as a Technique for PPAR Modulator Discovery.

Authors:  Stephanie N Lewis; Josep Bassaganya-Riera; David R Bevan
Journal:  PPAR Res       Date:  2009-09-02       Impact factor: 4.964

  9 in total

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