Literature DB >> 16454755

Recent developments in focused library design: targeting gene-families.

Jennifer L Miller1.   

Abstract

For many years, the most frequently optimized qualities of a screening library, or corporate compound collection, were size and diversity. Maximizing the number of diverse hits is the fundamental goal of such strategies. The ostensible justification that "bigger is better" is based on the large, estimated size of small-molecule space and the hypothesis that the notoriously low hit rates from high-throughput screening (HTS) could be overcome by brute force: i.e. by screening more compounds. Published, detailed studies about the success (or failure) of the brute-force strategy are rare, but it is well-known that it did not fulfill expectations. As a result, published reports in recent years have increasingly described methods for designing, selecting or synthesizing gene family-focused or -biased libraries. Moreover, many of the larger compound suppliers now sell such libraries, reflecting the growing interest in them from both the pharmaceutical and biotechnology markets. The trend towards gene family-focused libraries marks the emergence of a different hypothesis about how to increase HTS hit rates and also reflects an increasingly pragmatic focus on the management of screening libraries. An important, underlying assumption in this trend is that a high-quality, general-purpose screening library of manageable size is neither realizable nor desirable. Whether a biasing strategy based on a specific gene family will do a better job of meeting both the scientific and business needs of the drug discovery enterprise still remains to be seen, but it is certainly an active area of current research. This review focuses on the "who, what, why, when, and how" of the design of gene family-focused libraries. Particular attention is given to reports that discuss not only the techniques used, but also any results obtained.

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Year:  2006        PMID: 16454755     DOI: 10.2174/156802606775193347

Source DB:  PubMed          Journal:  Curr Top Med Chem        ISSN: 1568-0266            Impact factor:   3.295


  14 in total

1.  Privacy-preserving search for chemical compound databases.

Authors:  Kana Shimizu; Koji Nuida; Hiromi Arai; Shigeo Mitsunari; Nuttapong Attrapadung; Michiaki Hamada; Koji Tsuda; Takatsugu Hirokawa; Jun Sakuma; Goichiro Hanaoka; Kiyoshi Asai
Journal:  BMC Bioinformatics       Date:  2015-12-09       Impact factor: 3.169

2.  Novel Algorithms for the Identification of Biologically Informative Chemical Diversity Metrics.

Authors:  Bhargav Theertham; Jenna L Wang; Jianwen Fang; Gerald H Lushington
Journal:  Curr Comput Aided Drug Des       Date:  2008-03-01       Impact factor: 1.606

3.  New molecular scaffolds for the design of Mycobacterium tuberculosis type II dehydroquinase inhibitors identified using ligand and receptor based virtual screening.

Authors:  Ashutosh Kumar; Mohammad Imran Siddiqi; Stanislav Miertus
Journal:  J Mol Model       Date:  2009-10-09       Impact factor: 1.810

4.  Exploring key orientations at protein-protein interfaces with small molecule probes.

Authors:  Eunhwa Ko; Arjun Raghuraman; Lisa M Perez; Thomas R Ioerger; Kevin Burgess
Journal:  J Am Chem Soc       Date:  2012-12-27       Impact factor: 15.419

5.  Chemical Probe Identification Platform for Orphan GPCRs Using Focused Compound Screening: GPR39 as a Case Example.

Authors:  Markus Boehm; David Hepworth; Paula M Loria; Lisa D Norquay; Kevin J Filipski; Janice E Chin; Kimberly O Cameron; Martin Brenner; Peter Bonnette; Shawn Cabral; Edward Conn; David C Ebner; Denise Gautreau; John Hadcock; Esther C Y Lee; Alan M Mathiowetz; Michelle Morin; Lucy Rogers; Aaron Smith; Maria VanVolkenburg; Philip A Carpino
Journal:  ACS Med Chem Lett       Date:  2013-09-16       Impact factor: 4.345

6.  Pharmacological modulators of the circadian clock as potential therapeutic drugs.

Authors:  Marina P Antoch; Mikhail V Chernov
Journal:  Mutat Res       Date:  2009-08-14       Impact factor: 2.433

Review 7.  Rational methods for the selection of diverse screening compounds.

Authors:  David J Huggins; Ashok R Venkitaraman; David R Spring
Journal:  ACS Chem Biol       Date:  2011-02-15       Impact factor: 5.100

8.  Mining the ChEMBL database: an efficient chemoinformatics workflow for assembling an ion channel-focused screening library.

Authors:  N Yi Mok; Ruth Brenk
Journal:  J Chem Inf Model       Date:  2011-10-06       Impact factor: 4.956

9.  Identification and characterization of novel small-molecule inhibitors against hepatitis delta virus replication by using docking strategies.

Authors:  Sarita Singh; Sunil Kumar Gupta; Anuradha Nischal; Sanjay Khattri; Rajendra Nath; Kamlesh Kumar Pant; Prahlad Kishore Seth
Journal:  Hepat Mon       Date:  2011-10       Impact factor: 0.660

10.  Locating sweet spots for screening hits and evaluating pan-assay interference filters from the performance analysis of two lead-like libraries.

Authors:  N Yi Mok; Sara Maxe; Ruth Brenk
Journal:  J Chem Inf Model       Date:  2013-03-04       Impact factor: 4.956

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