TitleIdentifying promising compounds in drug discovery: Genetic algorithms and some new statistical techniques
Publication TypeJournal Article
Year of Publication2007
AuthorsMandal, A, Johnson, K, Wu, JCF, Bornemeier, D
JournalJOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume47
Pagination981-988
Date PublishedMAY-JUN
Type of ArticleArticle
ISSN1549-9596
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.

DOI10.1021/ci600556v
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