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

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).