Literature DB >> 21902278

REPROVIS-DB: a benchmark system for ligand-based virtual screening derived from reproducible prospective applications.

Peter Ripphausen1, Anne Mai Wassermann, Jürgen Bajorath.   

Abstract

Benchmark calculations are essential for the evaluation of virtual screening (VS) methods. Typically, classes of known active compounds taken from the medicinal chemistry literature are divided into reference molecules (search templates) and potential hits that are added to background databases assumed to consist of compounds not sharing this activity. Then VS calculations are carried out, and the recall of known active compounds is determined. However, conventional benchmarking is affected by a number of problems that reduce its value for method evaluation. In addition to often insufficient statistical validation and the lack of generally accepted evaluation standards, the artificial nature of typical benchmark settings is often criticized. Retrospective benchmark calculations generally overestimate the potential of VS methods and do not scale with their performance in prospective applications. In order to provide additional opportunities for benchmarking that more closely resemble practical VS conditions, we have designed a publicly available compound database (DB) of reproducible virtual screens (REPROVIS-DB) that organizes information from successful ligand-based VS applications including reference compounds, screening databases, compound selection criteria, and experimentally confirmed hits. Using the currently available 25 hand-selected compound data sets, one can attempt to reproduce successful virtual screens with other than the originally applied methods and assess their potential for practical applications.

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Year:  2011        PMID: 21902278     DOI: 10.1021/ci200309j

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  8 in total

1.  Benchmarking methods and data sets for ligand enrichment assessment in virtual screening.

Authors:  Jie Xia; Ermias Lemma Tilahun; Terry-Elinor Reid; Liangren Zhang; Xiang Simon Wang
Journal:  Methods       Date:  2014-12-03       Impact factor: 3.608

2.  Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis.

Authors:  Jie Xia; Terry-Elinor Reid; Song Wu; Liangren Zhang; Xiang Simon Wang
Journal:  J Chem Inf Model       Date:  2018-05-08       Impact factor: 4.956

3.  Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.

Authors:  Michael M Mysinger; Michael Carchia; John J Irwin; Brian K Shoichet
Journal:  J Med Chem       Date:  2012-07-05       Impact factor: 7.446

4.  Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications.

Authors:  Ye Hu; Jurgen Bajorath
Journal:  F1000Res       Date:  2012-08-14

5.  Follow up: Compound data sets and software tools for chemoinformatics and medicinal chemistry applications: update and data transfer.

Authors:  Ye Hu; Jürgen Bajorath
Journal:  F1000Res       Date:  2014-03-11

Review 6.  Biological Membrane-Penetrating Peptides: Computational Prediction and Applications.

Authors:  Ewerton Cristhian Lima de Oliveira; Kauê Santana da Costa; Paulo Sérgio Taube; Anderson H Lima; Claudomiro de Souza de Sales Junior
Journal:  Front Cell Infect Microbiol       Date:  2022-03-25       Impact factor: 5.293

7.  An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRs.

Authors:  Jie Xia; Hongwei Jin; Zhenming Liu; Liangren Zhang; Xiang Simon Wang
Journal:  J Chem Inf Model       Date:  2014-05-01       Impact factor: 4.956

8.  SimCAL: a flexible tool to compute biochemical reaction similarity.

Authors:  Tadi Venkata Sivakumar; Anirban Bhaduri; Rajasekhara Reddy Duvvuru Muni; Jin Hwan Park; Tae Yong Kim
Journal:  BMC Bioinformatics       Date:  2018-07-03       Impact factor: 3.169

  8 in total

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