Small-molecule screens---in which thousands or millions of molecules are tested in miniaturized assays---are commonly executed in both industry and academia. The molecules active in these screens can be starting points for new biological probes or therapeutics. These screens are expensive, and can be difficult to design.  In particular, deciding how many and which molecules to send for follow up testing is a critical task faced by all screeners. Common statistical tools used for this task are incomplete because do not simultaneously consider the diversity of hits, use structural similarity to reduce false positive rates, or consider the cost of molecules or the project's budget.  In a series of papers, we introduced an economic optimization that yields the only algorithms that simultaneously considers all these factors to select hits from a screen. Economic optimization provides a mathematical framework for balancing the complicated concerns at play in a screening project.

Using economic optimization to design high throughput screens

243rd National Meeting (2012)

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