Analysis of HTS Data

With these massive amounts of data produced in HTS for combinatorial libraries, tools become necessary to make it possible to navigate through these data and to extract the necessary information, to search - as is said quite often -for a needle in a haystack. 

A variety of methods have been developed by mathematicians and computer scientists to address this task, which has become known as data mining (see Chapter 9, Section 9.8). Fayyad defined and described the term data mining as the nontrivial extraction of impHcit, previously unknown and potentially useful information from data, or the search for relationships and global patterns that exist in databases [16]. In order to extract information from huge quantities of data and to gain knowledge from this information, the analysis and exploration have to be performed by automatic or semi-automatic methods. Methods applicable for data analysis are presented in Chapter 9. 

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There are three fundamental aspects in the analysis of HTS data (i) determining which compounds in a run or group of runs are active (ii) determining the potencies, efficacies, selectivities and mechanism of the actives and (iii) quality control. 

The current focus on early prediction of ADMET properties, as well as the analysis of HTS data, has led to a revival and extension of the use of QSAR technology in the pharmaceutical industry. Other applications can be found in metabonomics, the application of chemometrics to analytical spectral data to predict disease or the effect of compounds on metabolism. Many methods originally known in economics and artihcial intelligence research are now also being used in QSAR/QSPR. 

V Examples for ligand-based drug design A Lead optimization from the analysis of HTS data of a combinatorial library 138... 

Petit J, Meurice N, Mousses S, et al. Applications of rough sets theory in drug discovery Analysis of HTS data relative to the inhibition of Aurora A kinase. 236th National American Chemical Society Meeting 2008 Aug 17-21 Philadelphia. Division of Chemical Information Abstr. No. 40. 

This chapter discusses in more detail the operational informatics systems and their integration with compound management and HTS instrumentation. It then covers various aspects of screening data analysis such as statistical methods to define hits, quality considerations, reporting, visualization, and cheminformatics-driven analysis and mining of HTS data. We start from the assumption that the HTS assay has been optimized and miniaturized on an automation platform and that the assay has been validated to deliver robust and reproducible high quality results—the prerequisite of a successful HTS campaign. 

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). 

Having determined the order of the reaction, make the appropriate plot for each of the runs and determine the value of k (in concentration units) for each temperature. It is convenient to make these plots using A values rather than c values. The value of the slope can then be corrected, if necessary, to obtain k in concentration units by utilizing the value of sd. You should also carry out a least-squares analysis of your data using the appropriate form of Eq. (4). Report both sets of k values, together with the standard deviations for the least-squares values. Finally, plot log k versus HT and determine E. ... 

Examples of data warehouses published in literature are the SPINE [12] and CerBeruS [13] systems. While the SPINE system is focusing on the support and mining of protein crystallization data, the CerBeruS system links data relevant for the SAR analysis of larger data sets such as HTS data. 

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