Biased Complement Diversity Selection for Effective Exploration of Chemical Space in Hit-Finding Campaigns.

Authors:
Johanna M Jansen
Johanna M Jansen
Novartis Institutes for Biomedical Research
United States
Susan Fong
Susan Fong
Novartis Institutes for Biomedical Research
United States
Mika Lindvall
Mika Lindvall
University of Sheffield
Heinz E Moser
Heinz E Moser
The Ohio State University
United States
Keith Pfister
Keith Pfister
Novartis Institutes for Biomedical Research
United States
Bob Warne
Bob Warne
Novartis Institutes for Biomedical Research
United States
Charles Wartchow
Charles Wartchow
Stanford University
United States

J Chem Inf Model 2019 May 3;59(5):1709-1714. Epub 2019 Apr 3.

Novartis Institutes for BioMedical Research , 5300 Chiron Way , Emeryville , California 94608 , United States.

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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http://dx.doi.org/10.1021/acs.jcim.9b00048DOI Listing

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May 2019
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