Literature DB >> 22458679

Virtual compound screening in drug discovery.

Dagmar Stumpfe1, Peter Ripphausen, Jürgen Bajorath.   

Abstract

Virtual screening (VS) methods are applied in both academia and drug discovery, and can be divided into ligand- and target structure-based approaches. The VS field is still evolving and is characterized by scientific heterogeneity. The value of virtual compound screening for drug discovery is often debated, in particular, given the large investments made in experimental high-throughput screening technologies. The current state-of-the-art in the VS field is discussed. Despite its limitations, VS applications have often succeeded in identifying novel hits including first-in-class active compounds and novel chemotypes. VS has its place in pharmaceutical research, but there is still much room for further improvements including method evaluation and drug discovery applications. The potential of VS is currently underutilized because its complementarity to high-throughput screening is not sufficiently exploited. Building close interfaces between computational and experimental screening would further streamline the hit identification process.

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Year:  2012        PMID: 22458679     DOI: 10.4155/fmc.12.19

Source DB:  PubMed          Journal:  Future Med Chem        ISSN: 1756-8919            Impact factor:   3.808


  11 in total

Review 1.  Computational methods in drug discovery.

Authors:  Gregory Sliwoski; Sandeepkumar Kothiwale; Jens Meiler; Edward W Lowe
Journal:  Pharmacol Rev       Date:  2013-12-31       Impact factor: 25.468

Review 2.  CANDO and the infinite drug discovery frontier.

Authors:  Mark Minie; Gaurav Chopra; Geetika Sethi; Jeremy Horst; George White; Ambrish Roy; Kaushik Hatti; Ram Samudrala
Journal:  Drug Discov Today       Date:  2014-06-26       Impact factor: 7.851

3.  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

Review 4.  Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis.

Authors:  Tian Zhu; Shuyi Cao; Pin-Chih Su; Ram Patel; Darshan Shah; Heta B Chokshi; Richard Szukala; Michael E Johnson; Kirk E Hevener
Journal:  J Med Chem       Date:  2013-06-07       Impact factor: 7.446

Review 5.  Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology.

Authors:  Bruno O Villoutreix; Melaine A Kuenemann; Jean-Luc Poyet; Heriberto Bruzzoni-Giovanelli; Céline Labbé; David Lagorce; Olivier Sperandio; Maria A Miteva
Journal:  Mol Inform       Date:  2014-06-02       Impact factor: 3.353

6.  Plate-based diversity subset screening generation 2: an improved paradigm for high-throughput screening of large compound files.

Authors:  Andrew S Bell; Joseph Bradley; Jeremy R Everett; Jens Loesel; David McLoughlin; James Mills; Marie-Claire Peakman; Robert E Sharp; Christine Williams; Hongyao Zhu
Journal:  Mol Divers       Date:  2016-09-08       Impact factor: 2.943

Review 7.  Early state research on antifungal natural products.

Authors:  Melyssa Negri; Tânia P Salci; Cristiane S Shinobu-Mesquita; Isis R G Capoci; Terezinha I E Svidzinski; Erika Seki Kioshima
Journal:  Molecules       Date:  2014-03-07       Impact factor: 4.411

Review 8.  Mimicking Strategy for Protein-Protein Interaction Inhibitor Discovery by Virtual Screening.

Authors:  Ke-Jia Wu; Pui-Man Lei; Hao Liu; Chun Wu; Chung-Hang Leung; Dik-Lung Ma
Journal:  Molecules       Date:  2019-12-04       Impact factor: 4.411

Review 9.  Rational prediction with molecular dynamics for hit identification.

Authors:  Sara E Nichols; Robert V Swift; Rommie E Amaro
Journal:  Curr Top Med Chem       Date:  2012       Impact factor: 3.295

Review 10.  Computational approaches in target identification and drug discovery.

Authors:  Theodora Katsila; Georgios A Spyroulias; George P Patrinos; Minos-Timotheos Matsoukas
Journal:  Comput Struct Biotechnol J       Date:  2016-05-07       Impact factor: 7.271

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