HTS data analysis

There is a growing interest in HTS data analysis and hit selection, as indicated by the plentiful commercial offerings from companies such as BioReason (ClassPharmer... 

Multiple Feature Tree Models Applications in HTS Data Analysis... 

Statistical HTS data analysis and discovery cheminformatics are integral components of an HTS-driven lead discovery process. Various statistical and visualization techniques are routinely... 

Shortfalls of the common control-based HTS data analysis approach have been suggested (Gribbon et al., 2005 Malo et al., 2006). For example plate-based normalization using controls (Table 14.1) intrinsically assumes a random error distribution for all wells in a plate because of the positional bias of the controls dictated by the formats of common screening libraries. Edge effects are particularly relevant in cell-based assays and vary from plate to plate (Malo et al., 2006). Classical HTS analysis relies on non-robust statistics means and standard deviations are greatly influenced by outliers. 

Structure-based clustering is used to group related compounds for the purpose of HTS data analysis, identification of SAR series, and detection of potential outliers (Engels et al., 2002). In one example, researchers at GNF reported a statistical approach to dynamically score each scaffold family (obtained by prior clustering of screened structures) based on family members HTS activities. This method identifies compounds that share structural similarities and similarly high HTS activities it yielded greatly improved confirmation rates compared to using a static (scaffold-independent) activity cut-off (Yan et al., 2005). 

Hohenberg-Kohn (HK) theorem, 1,11, 20 homodesmotic reactions, 1, 34 homology models, 1,170,188,189 3, 211 HTD see high throughput docking HTS data analysis, 2, 156... 

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