Literature DB >> 12546933

Why do we need so many chemical similarity search methods?

Robert P Sheridan1, Simon K Kearsley.   

Abstract

Computational tools to search chemical structure databases are essential to finding leads early in a drug discovery project. Similarity methods are among the most diverse and most useful. We will present some lessons we have gathered over many years experience with in-house methods on several therapeutic problems. The effectiveness of any similarity method can vary greatly from one biological activity to another in a way that is difficult to predict. Also, any two methods tend to select different subsets of actives from a database, so it is advisable to use several search methods where possible.

Mesh:

Year:  2002        PMID: 12546933     DOI: 10.1016/s1359-6446(02)02411-x

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  73 in total

1.  Ligand expansion in ligand-based virtual screening using relevance feedback.

Authors:  Ammar Abdo; Faisal Saeed; Hentabli Hamza; Ali Ahmed; Naomie Salim
Journal:  J Comput Aided Mol Des       Date:  2012-01-17       Impact factor: 3.686

Review 2.  QSAR without borders.

Authors:  Eugene N Muratov; Jürgen Bajorath; Robert P Sheridan; Igor V Tetko; Dmitry Filimonov; Vladimir Poroikov; Tudor I Oprea; Igor I Baskin; Alexandre Varnek; Adrian Roitberg; Olexandr Isayev; Stefano Curtarolo; Denis Fourches; Yoram Cohen; Alan Aspuru-Guzik; David A Winkler; Dimitris Agrafiotis; Artem Cherkasov; Alexander Tropsha
Journal:  Chem Soc Rev       Date:  2020-05-01       Impact factor: 54.564

3.  Measuring CAMD technique performance: a virtual screening case study in the design of validation experiments.

Authors:  Andrew C Good; Mark A Hermsmeier; S A Hindle
Journal:  J Comput Aided Mol Des       Date:  2004 Jul-Sep       Impact factor: 3.686

4.  Charting biologically relevant chemical space: a structural classification of natural products (SCONP).

Authors:  Marcus A Koch; Ansgar Schuffenhauer; Michael Scheck; Stefan Wetzel; Marco Casaulta; Alex Odermatt; Peter Ertl; Herbert Waldmann
Journal:  Proc Natl Acad Sci U S A       Date:  2005-11-21       Impact factor: 11.205

5.  BRUTUS: optimization of a grid-based similarity function for rigid-body molecular superposition. II. Description and characterization.

Authors:  Toni Rönkkö; Anu J Tervo; Jussi Parkkinen; Antti Poso
Journal:  J Comput Aided Mol Des       Date:  2006-07-20       Impact factor: 3.686

6.  Similarity study of serine proteases inhibitors.

Authors:  Gleb D Perekhodtsev
Journal:  Mol Divers       Date:  2006-02       Impact factor: 2.943

Review 7.  Evaluation of machine-learning methods for ligand-based virtual screening.

Authors:  Beining Chen; Robert F Harrison; George Papadatos; Peter Willett; David J Wood; Xiao Qing Lewell; Paulette Greenidge; Nikolaus Stiefl
Journal:  J Comput Aided Mol Des       Date:  2007-01-05       Impact factor: 3.686

8.  Reverse fingerprinting, similarity searching by group fusion and fingerprint bit importance.

Authors:  Chris Williams
Journal:  Mol Divers       Date:  2006-09-21       Impact factor: 2.943

Review 9.  Chemogenomic approaches to rational drug design.

Authors:  D Rognan
Journal:  Br J Pharmacol       Date:  2007-05-29       Impact factor: 8.739

Review 10.  Machine learning in chemoinformatics and drug discovery.

Authors:  Yu-Chen Lo; Stefano E Rensi; Wen Torng; Russ B Altman
Journal:  Drug Discov Today       Date:  2018-05-08       Impact factor: 7.851

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