Literature DB >> 23484962

What has virtual screening ever done for drug discovery?

David E Clark1.   

Abstract

BACKGROUND: Although virtual screening is now widely applied as a hit-finding methodology within drug discovery programmes, there are relatively few reports of its contributing to compounds on the market or in the clinic.
OBJECTIVE: To assess the impact of virtual screening on drug discovery.
METHOD: Such cases as can be found in the public domain at the current time are reviewed. Additionally, some of the current challenges in structure- and ligand-based virtual screening are discussed.
CONCLUSION: It is concluded that virtual screening has contributed to the discovery of several compounds that have either reached the market or entered clinical trials. In terms of praxis, there is 'no free lunch' in virtual screening and as many methods as possible should be applied to maximise the likelihood of success.

Year:  2008        PMID: 23484962     DOI: 10.1517/17460441.3.8.841

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


  30 in total

Review 1.  Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.

Authors:  Linlin Zhao; Heather L Ciallella; Lauren M Aleksunes; Hao Zhu
Journal:  Drug Discov Today       Date:  2020-07-11       Impact factor: 7.851

2.  Frog2: Efficient 3D conformation ensemble generator for small compounds.

Authors:  Maria A Miteva; Frederic Guyon; Pierre Tufféry
Journal:  Nucleic Acids Res       Date:  2010-05-05       Impact factor: 16.971

3.  Designing focused chemical libraries enriched in protein-protein interaction inhibitors using machine-learning methods.

Authors:  Christelle Reynès; Hélène Host; Anne-Claude Camproux; Guillaume Laconde; Florence Leroux; Anne Mazars; Benoit Deprez; Robin Fahraeus; Bruno O Villoutreix; Olivier Sperandio
Journal:  PLoS Comput Biol       Date:  2010-03-05       Impact factor: 4.475

4.  Molecular shape and medicinal chemistry: a perspective.

Authors:  Anthony Nicholls; Georgia B McGaughey; Robert P Sheridan; Andrew C Good; Gregory Warren; Magali Mathieu; Steven W Muchmore; Scott P Brown; J Andrew Grant; James A Haigh; Neysa Nevins; Ajay N Jain; Brian Kelley
Journal:  J Med Chem       Date:  2010-05-27       Impact factor: 7.446

5.  Prediction of potency of protease inhibitors using free energy simulations with polarizable quantum mechanics-based ligand charges and a hybrid water model.

Authors:  Debananda Das; Yasuhiro Koh; Yasushi Tojo; Arun K Ghosh; Hiroaki Mitsuya
Journal:  J Chem Inf Model       Date:  2009-12       Impact factor: 4.956

6.  Discover binding pathways using the sliding binding-box docking approach: application to binding pathways of oseltamivir to avian influenza H5N1 neuraminidase.

Authors:  Diem-Trang T Tran; Ly T Le; Thanh N Truong
Journal:  J Comput Aided Mol Des       Date:  2013-08-24       Impact factor: 3.686

7.  Molecular docking studies of gyrase inhibitors: weighing earlier screening bedrock.

Authors:  H S Santosh Kumar; S Ravi Kumar; N Naveen Kumar; S Ajith
Journal:  In Silico Pharmacol       Date:  2021-01-01

Review 8.  Structure-based virtual screening for drug discovery: a problem-centric review.

Authors:  Tiejun Cheng; Qingliang Li; Zhigang Zhou; Yanli Wang; Stephen H Bryant
Journal:  AAPS J       Date:  2012-01-27       Impact factor: 4.009

9.  Identification of novel antimalarial chemotypes via chemoinformatic compound selection methods for a high-throughput screening program against the novel malarial target, PfNDH2: increasing hit rate via virtual screening methods.

Authors:  Raman Sharma; Alexandre S Lawrenson; Nicholas E Fisher; Ashley J Warman; Alison E Shone; Alasdair Hill; Alison Mbekeani; Chandrakala Pidathala; Richard K Amewu; Suet Leung; Peter Gibbons; David W Hong; Paul Stocks; Gemma L Nixon; James Chadwick; Joanne Shearer; Ian Gowers; David Cronk; Serge P Parel; Paul M O'Neill; Stephen A Ward; Giancarlo A Biagini; Neil G Berry
Journal:  J Med Chem       Date:  2012-03-22       Impact factor: 7.446

10.  Enhanced ranking of PknB Inhibitors using data fusion methods.

Authors:  Abhik Seal; Perumal Yogeeswari; Dharmaranjan Sriram; David J Wild
Journal:  J Cheminform       Date:  2013-01-14       Impact factor: 5.514

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