Literature DB >> 23330660

How far can virtual screening take us in drug discovery?

Supratik Kar, Kunal Roy.   

Abstract

INTRODUCTION: Virtual screening (VS) has emerged as an important tool in identifying bioactive compounds through computational means, by employing knowledge about the protein target or known bioactive ligands. VS has appeared as an adaptive response to the massive throughput synthesis and screening paradigm as necessity has forced the computational chemistry community to develop tools that screen against any given target and/or property millions or perhaps billions of molecules in short period of time. AREAS COVERED: This editorial review attempts to catalog most commonly exercised VS methods, available databases for screening, advantages of VS methods along with pitfalls and technical traps with the aim to make VS as one of the most effective tools in drug discovery process. Finally, several case studies are cited where the VS technology has been applied successfully. EXPERT OPINION: In recent times, many successful examples have been demonstrated in the field of computer-aided VS with the objective of increasing the probability of finding novel hit and lead compounds in terms of cost-effectiveness and commitment in time and material. Despite the inherent limitations, VS is still the best option now available to explore a large chemical space.

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Year:  2013        PMID: 23330660     DOI: 10.1517/17460441.2013.761204

Source DB:  PubMed          Journal:  Expert Opin Drug Discov        ISSN: 1746-0441            Impact factor:   6.098


  28 in total

Review 1.  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

2.  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

3.  DARC: Mapping Surface Topography by Ray-Casting for Effective Virtual Screening at Protein Interaction Sites.

Authors:  Ragul Gowthaman; Sven A Miller; Steven Rogers; Jittasak Khowsathit; Lan Lan; Nan Bai; David K Johnson; Chunjing Liu; Liang Xu; Asokan Anbanandam; Jeffrey Aubé; Anuradha Roy; John Karanicolas
Journal:  J Med Chem       Date:  2015-07-10       Impact factor: 7.446

4.  In Silico Tools and Software to Predict ADMET of New Drug Candidates.

Authors:  Supratik Kar; Kunal Roy; Jerzy Leszczynski
Journal:  Methods Mol Biol       Date:  2022

5.  Validation of Deep Learning-Based DFCNN in Extremely Large-Scale Virtual Screening and Application in Trypsin I Protease Inhibitor Discovery.

Authors:  Haiping Zhang; Xiao Lin; Yanjie Wei; Huiling Zhang; Linbu Liao; Hao Wu; Yi Pan; Xuli Wu
Journal:  Front Mol Biosci       Date:  2022-06-01

6.  Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery.

Authors:  Raquel Rodríguez-Pérez; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2022-03-19       Impact factor: 4.179

Review 7.  Modern approaches to accelerate discovery of new antischistosomal drugs.

Authors:  Bruno Junior Neves; Eugene Muratov; Renato Beilner Machado; Carolina Horta Andrade; Pedro Vitor Lemos Cravo
Journal:  Expert Opin Drug Discov       Date:  2016-05-03       Impact factor: 6.098

8.  Applying DEKOIS 2.0 in structure-based virtual screening to probe the impact of preparation procedures and score normalization.

Authors:  Tamer M Ibrahim; Matthias R Bauer; Frank M Boeckler
Journal:  J Cheminform       Date:  2015-05-20       Impact factor: 5.514

9.  Biotechnology landscape in cancer drug discovery.

Authors:  Monica Neagu; Radu Albulescu; Cristiana Tanase
Journal:  Future Sci OA       Date:  2015-11-01

10.  Academic drug discovery within the United Kingdom: a reassessment.

Authors:  Emma Shanks; Robin Ketteler; Daniel Ebner
Journal:  Nat Rev Drug Discov       Date:  2015-06-19       Impact factor: 84.694

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