Literature DB >> 26038804

Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives.

Nathalie Lagarde1, Jean-François Zagury1, Matthieu Montes1.   

Abstract

Virtual screening methods are commonly used nowadays in drug discovery processes. However, to ensure their reliability, they have to be carefully evaluated. The evaluation of these methods is often realized in a retrospective way, notably by studying the enrichment of benchmarking data sets. To this purpose, numerous benchmarking data sets were developed over the years, and the resulting improvements led to the availability of high quality benchmarking data sets. However, some points still have to be considered in the selection of the active compounds, decoys, and protein structures to obtain optimal benchmarking data sets.

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Year:  2015        PMID: 26038804     DOI: 10.1021/acs.jcim.5b00090

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


  21 in total

1.  Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations.

Authors:  Kai Liu; Etsurou Watanabe; Hironori Kokubo
Journal:  J Comput Aided Mol Des       Date:  2017-01-10       Impact factor: 3.686

2.  Predicting protein-ligand affinity with a random matrix framework.

Authors:  Alpha A Lee; Michael P Brenner; Lucy J Colwell
Journal:  Proc Natl Acad Sci U S A       Date:  2016-11-16       Impact factor: 11.205

3.  A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor.

Authors:  Maria E Mavrogeni; Filippos Pronios; Danae Zareifi; Sofia Vasilakaki; Olivier Lozach; Leonidas Alexopoulos; Laurent Meijer; Vassilios Myrianthopoulos; Emmanuel Mikros
Journal:  Future Med Chem       Date:  2018-10-16       Impact factor: 3.808

4.  Crystal structure of type II NADH:quinone oxidoreductase from Caldalkalibacillus thermarum with an improved resolution of 2.15 Å.

Authors:  Yoshio Nakatani; Wanting Jiao; David Aragão; Yosuke Shimaki; Jessica Petri; Emily J Parker; Gregory M Cook
Journal:  Acta Crystallogr F Struct Biol Commun       Date:  2017-09-23       Impact factor: 1.056

Review 5.  Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery.

Authors:  Maykel Cruz-Monteagudo; Stephan Schürer; Eduardo Tejera; Yunierkis Pérez-Castillo; José L Medina-Franco; Aminael Sánchez-Rodríguez; Fernanda Borges
Journal:  Drug Discov Today       Date:  2017-03-06       Impact factor: 7.851

6.  Fine tuning for success in structure-based virtual screening.

Authors:  Emilie Pihan; Martin Kotev; Obdulia Rabal; Claudia Beato; Constantino Diaz Gonzalez
Journal:  J Comput Aided Mol Des       Date:  2021-11-20       Impact factor: 3.686

7.  Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging.

Authors:  Grigorii V Andrianov; Wern Juin Gabriel Ong; Ilya Serebriiskii; John Karanicolas
Journal:  J Chem Inf Model       Date:  2021-11-11       Impact factor: 4.956

8.  Ligand Strain Energy in Large Library Docking.

Authors:  Shuo Gu; Matthew S Smith; Ying Yang; John J Irwin; Brian K Shoichet
Journal:  J Chem Inf Model       Date:  2021-09-01       Impact factor: 6.162

9.  Virtual screening and drug repurposing experiments to identify potential novel selective MAO-B inhibitors for Parkinson's disease treatment.

Authors:  Luminita Crisan; Daniela Istrate; Alina Bora; Liliana Pacureanu
Journal:  Mol Divers       Date:  2020-11-25       Impact factor: 2.943

Review 10.  Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.

Authors:  Ahmet Sureyya Rifaioglu; Heval Atas; Maria Jesus Martin; Rengul Cetin-Atalay; Volkan Atalay; Tunca Doğan
Journal:  Brief Bioinform       Date:  2019-09-27       Impact factor: 11.622

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