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Similarity Searching using Compound Class‐Specific Combinations of Substructures Found in Randomly Generated Molecular Fragment Populations
Author(s) -
Batista José,
Bajorath Jürgen
Publication year - 2008
Publication title -
chemmedchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.817
H-Index - 100
eISSN - 1860-7187
pISSN - 1860-7179
DOI - 10.1002/cmdc.200700199
Subject(s) - fragment (logic) , similarity (geometry) , fingerprint (computing) , computer science , string (physics) , class (philosophy) , data mining , computational biology , information retrieval , nearest neighbor search , pattern recognition (psychology) , artificial intelligence , algorithm , mathematics , biology , image (mathematics) , mathematical physics
Fine fingerprints . Herein, we show that combinations of randomly generated fragments are signatures of active molecules. Small sets of such fragments are encoded as bit string representations and used for similarity searching. These fingerprints are successfully applied to mine high‐throughput screening data sets. Shown are randomly generated substructures encoded as a small fingerprint that were extracted from a fragment pathway specific for cathepsin B inhibitors.