Abstract：
:Throughout the drug discovery process, discovery teams are compelled to use statistics for making decisions using data from a variety of inputs. For instance, teams are asked to prioritize compounds for subsequent stages of the drug discovery process, given results from multiple screens. To assist in the prioritization process, we propose a desirability function to account for a priori scientific knowledge; compounds can then be prioritized based on their desirability scores. In addition to identifying existing desirable compounds, teams often use prior knowledge to suggest new, potentially promising compounds to be created in the laboratory. Because the chemistry space to search can be dauntingly large, we propose the sequential elimination of level combinations (SELC) method for identifying new optimal compounds. We illustrate this method on a combinatorial chemistry example.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Mandal A,Johnson K,Wu CF,Bornemeier Ddoi
10.1021/ci600556vsubject
Has Abstractpub_date
2007-05-01 00:00:00pages
981-8issue
3eissn
1549-9596issn
1549-960Xjournal_volume
47pub_type
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