Literature DB >> 23651302

Virtual screening strategies in drug discovery: a critical review.

A Lavecchia1, C Di Giovanni.   

Abstract

Virtual screening (VS) is a powerful technique for identifying hit molecules as starting points for medicinal chemistry. The number of methods and softwares which use the ligand and target-based VS approaches is increasing at a rapid pace. What, however, are the real advantages and disadvantages of the VS technology and how applicable is it to drug discovery projects? This review provides a comprehensive appraisal of several VS approaches currently available. In the first part of this work, an overview of the recent progress and advances in both ligand-based VS (LBVS) and structure-based VS (SBVS) strategies highlighting current problems and limitations will be provided. Special emphasis will be given to in silico chemogenomics approaches which utilize annotated ligand-target as well as protein-ligand interaction databases and which could predict or reveal promiscuous binding and polypharmacology, the knowledge of which would help medicinal chemists to design more potent clinical candidates with fewer side effects. In the second part, recent case studies (all published in the last two years) will be discussed where the VS technology has been applied successfully. A critical analysis of these case studies provides a good platform in order to estimate the applicability of various VS strategies in the new lead identification and optimization.

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Year:  2013        PMID: 23651302     DOI: 10.2174/09298673113209990001

Source DB:  PubMed          Journal:  Curr Med Chem        ISSN: 0929-8673            Impact factor:   4.530


  121 in total

1.  Study of intra-inter species protein-protein interactions for potential drug targets identification and subsequent drug design for Escherichia coli O104:H4 C277-11.

Authors:  Shakhinur Islam Mondal; Zabed Mahmud; Montasir Elahi; Arzuba Akter; Nurnabi Azad Jewel; Md Muzahidul Islam; Sabiha Ferdous; Taisei Kikuchi
Journal:  In Silico Pharmacol       Date:  2017-04-11

2.  Identification of New Human Malaria Parasite Plasmodium falciparum Dihydroorotate Dehydrogenase Inhibitors by Pharmacophore and Structure-Based Virtual Screening.

Authors:  Elumalai Pavadai; Farah El Mazouni; Sergio Wittlin; Carmen de Kock; Margaret A Phillips; Kelly Chibale
Journal:  J Chem Inf Model       Date:  2016-03-08       Impact factor: 4.956

3.  A computational approach yields selective inhibitors of human excitatory amino acid transporter 2 (EAAT2).

Authors:  Kelly L Damm-Ganamet; Marie-Laure Rives; Alan D Wickenden; Heather M McAllister; Taraneh Mirzadegan
Journal:  J Biol Chem       Date:  2020-02-20       Impact factor: 5.157

4.  Ligand-based virtual screening under partial shape constraints.

Authors:  Mathias M von Behren; Matthias Rarey
Journal:  J Comput Aided Mol Des       Date:  2017-03-18       Impact factor: 3.686

5.  In Silico Prediction of Major Clearance Pathways of Drugs among 9 Routes with Two-Step Support Vector Machines.

Authors:  Naomi Wakayama; Kota Toshimoto; Kazuya Maeda; Shun Hotta; Takashi Ishida; Yutaka Akiyama; Yuichi Sugiyama
Journal:  Pharm Res       Date:  2018-08-24       Impact factor: 4.200

Review 6.  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

7.  LBVS: an online platform for ligand-based virtual screening using publicly accessible databases.

Authors:  Minghao Zheng; Zhihong Liu; Xin Yan; Qianzhi Ding; Qiong Gu; Jun Xu
Journal:  Mol Divers       Date:  2014-09-03       Impact factor: 2.943

8.  Prospective evaluation of shape similarity based pose prediction method in D3R Grand Challenge 2015.

Authors:  Ashutosh Kumar; Kam Y J Zhang
Journal:  J Comput Aided Mol Des       Date:  2016-08-02       Impact factor: 3.686

9.  Enhancing Virtual Screening Performance of Protein Kinases with Molecular Dynamics Simulations.

Authors:  Tavina L Offutt; Robert V Swift; Rommie E Amaro
Journal:  J Chem Inf Model       Date:  2016-10-03       Impact factor: 4.956

10.  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

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