Biased Complement Diversity Selection for Effective Exploration of Chemical Space in Hit-Finding Campaigns
Author(s) -
Johanna M. Jansen,
Gianfranco De Pascale,
Susan Fong,
Mika Lindvall,
Heinz E. Moser,
Keith Pfister,
Bob Warne,
Charles Wartchow
Publication year - 2019
Publication title -
journal of chemical information and modeling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 160
eISSN - 1549-960X
pISSN - 1549-9596
DOI - 10.1021/acs.jcim.9b00048
Subject(s) - complement (music) , workflow , chemical space , diversity (politics) , computer science , set (abstract data type) , selection (genetic algorithm) , function (biology) , quality (philosophy) , space (punctuation) , data science , machine learning , drug discovery , bioinformatics , biology , database , programming language , genetics , phenotype , operating system , philosophy , epistemology , complementation , sociology , gene , anthropology
The success of hit-finding campaigns relies on many factors, including the quality and diversity of the set of compounds that is selected for screening. This paper presents a generalized workflow that guides compound selections from large compound archives with opportunities to bias the selections with available knowledge in order to improve hit quality while still effectively sampling the accessible chemical space. An optional flag in the workflow supports an explicit complement design function where diversity selections complement a given core set of compounds. Results from three project applications as well as a literature case study exemplify the effectiveness of the approach, which is available as a KNIME workflow named Biased Complement Diversity (BCD).
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