Managing missing measurements in small-molecule screens.

In a typical high-throughput screening (HTS) campaign, less than 1 % of the small-molecule library is characterized by confirmatory experiments. As much as 99 % of the library's molecules are set aside--and not included in downstream analysis--although some of these molecules would prove active were they sent for confirmatory testing. These missing experimental measurements prevent active molecules from being identified by screeners.

Automatically detecting workflows in PubChem.

Public databases that store the data from small-molecule screens are a rich and untapped resource of chemical and biological information. However, screening databases are unorganized, which makes interpreting their data difficult. We propose a method of inferring workflow graphs--which encode the relationships between assays in screening projects--directly from screening data and using these workflows to organize each project's data.

Utility-aware screening with clique-oriented prioritization.

Most methods of deciding which hits from a screen to send for confirmatory testing assume that all confirmed actives are equally valuable and aim only to maximize the number of confirmed hits. In contrast, "utility-aware" methods are informed by models of screeners' preferences and can increase the rate at which the useful information is discovered. Clique-oriented prioritization (COP) extends a recently proposed economic framework and aims--by changing which hits are sent for confirmatory testing--to maximize the number of scaffolds with at least two confirmed active examples.

Probabilistic Substructure Mining From Small-Molecule Screens.

Identifying the overrepresented substructures from a set of molecules with similar activity is a common task in chemical informatics. Existing substructure miners are deterministic, requiring the activity of all mined molecules to be known with high confidence. In contrast, we introduce pGraphSig, a probabilistic structure miner, which effectively mines structures from noisy data, where many molecules are labeled with their probability of being active.

Enhancing the rate of scaffold discovery with diversity-oriented prioritization.

Motivation: In high-throughput screens (HTS) of small molecules for activity in an in vitro assay, it is common to search for active scaffolds, with at least one example successfully confirmed as an active. The number of active scaffolds better reflects the success of the screen than the number of active molecules. Many existing algorithms for deciding which hits should be sent for confirmatory testing neglect this concern.