Literature DB >> 12773164

Similarity-based approaches to virtual screening.

P Willett1.   

Abstract

Current similarity measures for virtual screening are based on the use of molecular fingerprints and the Tanimoto coefficient. This paper describes two ways in which one can increase the effectiveness of similarity-based virtual screening: using similarity coefficients other than the Tanimoto coefficient for the comparison of molecular fingerprints; and using a graph-theoretic similarity measure based on the largest substructure common to a pair of molecules.

Mesh:

Year:  2003        PMID: 12773164     DOI: 10.1042/bst0310603

Source DB:  PubMed          Journal:  Biochem Soc Trans        ISSN: 0300-5127            Impact factor:   5.407


  31 in total

Review 1.  Methods for Similarity-based Virtual Screening.

Authors:  Thomas G Kristensen; Jesper Nielsen; Christian N S Pedersen
Journal:  Comput Struct Biotechnol J       Date:  2013-03-03       Impact factor: 7.271

2.  Discovery of protein phosphatase 2C inhibitors by virtual screening.

Authors:  Jessica P Rogers; Albert E Beuscher; Marc Flajolet; Thomas McAvoy; Angus C Nairn; Arthur J Olson; Paul Greengard
Journal:  J Med Chem       Date:  2006-03-09       Impact factor: 7.446

Review 3.  A cheminformatic toolkit for mining biomedical knowledge.

Authors:  Gus R Rosania; Gordon Crippen; Peter Woolf; David States; Kerby Shedden
Journal:  Pharm Res       Date:  2007-03-24       Impact factor: 4.200

Review 4.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

5.  Search for non-nucleoside inhibitors of HIV-1 reverse transcriptase using chemical similarity, molecular docking, and MM-GB/SA scoring.

Authors:  Gabriela Barreiro; Cristiano R W Guimarães; Ivan Tubert-Brohman; Theresa M Lyons; Julian Tirado-Rives; William L Jorgensen
Journal:  J Chem Inf Model       Date:  2007-10-20       Impact factor: 4.956

6.  Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets.

Authors:  William J Allen; Brian C Fochtman; Trent E Balius; Robert C Rizzo
Journal:  J Comput Chem       Date:  2017-09-22       Impact factor: 3.376

7.  Shallow Representation Learning via Kernel PCA Improves QSAR Modelability.

Authors:  Stefano E Rensi; Russ B Altman
Journal:  J Chem Inf Model       Date:  2017-08-07       Impact factor: 4.956

Review 8.  The importance of discerning shape in molecular pharmacology.

Authors:  Sandhya Kortagere; Matthew D Krasowski; Sean Ekins
Journal:  Trends Pharmacol Sci       Date:  2009-01-31       Impact factor: 14.819

9.  Evaluation of computational docking to identify pregnane X receptor agonists in the ToxCast database.

Authors:  Sandhya Kortagere; Matthew D Krasowski; Erica J Reschly; Madhukumar Venkatesh; Sridhar Mani; Sean Ekins
Journal:  Environ Health Perspect       Date:  2010-06-17       Impact factor: 9.031

10.  A tree-based method for the rapid screening of chemical fingerprints.

Authors:  Thomas G Kristensen; Jesper Nielsen; Christian N S Pedersen
Journal:  Algorithms Mol Biol       Date:  2010-01-04       Impact factor: 1.405

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