Mining High‐Throughput Screening Data by Novel Knowledge‐Based Optimization Analysis

Yan, Shucheng; King, Frederick J.; Chanda, Sumit K.; Caldwell, Jeremy S.; Winzeler, Elizabeth A.; Zhou, Yingyao

doi:10.1002/9780470567623.ch7

Cited by 1 publication

(2 citation statements)

References 54 publications

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“…Instead of arbitrarily setting an f H threshold (e.g., 0.1), we applied an OPI algorithm to identify frequent hits from various HTS assay sets, either by a scaffold-driven or an MOA-driven approach. The detailed steps of the algorithm were described in a review, and it has prior applications to large HTS data sets. , Ontology-based pattern identification (OPI) algorithm provides a nonheuristic alternative to identify frequent hits, which minimizes the false positives and only identifies compounds whose chemotypes or MOAs were robustly associated with high-frequency hit patterns. We outline the key steps of the algorithm that were employed in this study below.…”

Section: Methodsmentioning

confidence: 99%

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Chemical and Biological Properties of Frequent Screening Hits

Che

King

Zhou

et al. 2012

J. Chem. Inf. Model.

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High-throughput screening (HTS) has become an important technology for the drug discovery process. It has been noted that certain compounds frequently appear as hits in many screening campaigns. By mining an HTS database covering large chemical space and diverse biological functions, we identified many novel chemical features, as well as several biological processes that were associated with a significant portion of frequent hits. However, we also noted that several marketed drugs also contained characteristics that commonly were associated with frequent hits. This observation suggested that current generally employed strategies for triaging compounds may result in the removal of compounds with desirable properties. Therefore, we developed a novel strategy that overlaid chemical scaffolds and biological processes, along with empirical hit frequency data, in order to provide a more functional frequent hit triage strategy; the risk of removing biologically relevant frequent hits was reduced compared to the typical empirical hit frequency-based filtering strategy.

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Section: Methodsmentioning

confidence: 99%

“…Next, we applied the OPI 17 algorithm to all of the cell-based and biochemical assays, respectively (see Materials and Methods). The frequent hits were identified with p-values less than 0.05.…”

Section: ■ Introductionmentioning

confidence: 99%