Literature DB >> 16475969

Comparison of methods for sequential screening of large compound sets.

Paul E Blower1, Kevin P Cross, Gabriel S Eichler, Glenn J Myatt, John N Weinstein, Chihae Yang.   

Abstract

Sequential screening is an iterative procedure that can greatly increase hit rates over random screening or non-iterative procedures. We studied the effects of three factors on enrichment rates: the method used to rank compounds, the molecular descriptor set and the selection of initial training set. The primary factor influencing recovery rates was the method of selecting the initial training set. Rates for recovering active compounds were substantially lower with the diverse training sets than they were with training sets selected by other methods. Because structure-activity information is incrementally enhanced in intermediate training sets, sequential screening provides significant improvement in the average rate of recovery of active compounds when compared with non-iterative selection procedures.

Mesh:

Year:  2006        PMID: 16475969     DOI: 10.2174/138620706775541882

Source DB:  PubMed          Journal:  Comb Chem High Throughput Screen        ISSN: 1386-2073            Impact factor:   1.339


  2 in total

1.  A maximum common substructure-based algorithm for searching and predicting drug-like compounds.

Authors:  Yiqun Cao; Tao Jiang; Thomas Girke
Journal:  Bioinformatics       Date:  2008-07-01       Impact factor: 6.937

2.  The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data.

Authors:  Gabriel S Eichler; Mark Reimers; David Kane; John N Weinstein
Journal:  Genome Biol       Date:  2007       Impact factor: 13.583

  2 in total

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