Hit Quality

Use mathematical methods, such as Hit quality index (HQI) value (or similarity value vs. library reference sample). An example list of such HQI methods includes... 

The results of data treatment are documented and evaluated in ES 5 and the interpretation in ES 6 is guided by the analyst s constraints and requirements. For instance, simple visual pattern comparisions may be acceptable for sample identification, or a combined database (GC-FTIR/GC-MS), (PGC/FTIR), (GC/TA), etc., analysis may be required. Judgmental decisions must be trained into the system as to depth of analysis, its acceptability and reliability (e.g., the hit quality index (HQI) of the MS search combined with that from the FTIR search may confirm within a 95% confidence level the GC peak or sample identity). 

Enhancing Hit Quality and Diversity Within Assay Throughput Constraints... 

MacFadyenH, Walker G, Alvarez J. (2005) Enhancing hit quality and diversity within assay throughput constraints. In T Oprea (ed), Chemoinformatics in Drug Discovery, pp. 143-174. Wiley-VCH, Weinheim. 

However, after a detailed inspection of the results of these screening campaigns, it is now evident that neither hit rates (11) nor hit quality (e.g., unsuitable functional groups, poor solubility of identified hits (12)) obtained from HTS experiments have shown any significant improvements over time. 

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. 

The confidence level in the identificadon capabiUty for gc/ir/ms is enhanced by the ability to perform computer library searching of large spectral databases. Unknown spectra are searched against reference databases and a hit quality number, indicating how well the unknown spectrum matches the library spectra, is generated. For the very small peak at 19 min in Figure 4, peak 6 in the TIC, the library search identified the component as a-phellandrene [99-83-2]y for both the ic and ms data. Because these data are complementary and generated from two completely independent principles of... 

The hit quality index (HQI) ranks the candidate reference spectra by their similarity to the unknown spectrum. As warned above it is not wise to take the hit list ranked by HQI at face value but to look down the hit list where it may well be possible to find what a human expert would regard as a better candidate solution to the search than the algorithm has picked out. 

A report data block mnwrl will be appended to every spectrum that was compared to the reference spectrum. This Report of Correlation Search (see Fig. 11.24) lists the Hit Quality , the sample name (as taken from the spectrum parameter block), and the file name. The hit quality should preferably be a small number, with zero being an absolute match. The exemplary report listed in Fig. 11.24 proves that the unknown sample 2 is cocaine the hit quality is 0.000000. 

Maximum Number of Hits The number of hits that are to be saved in the search report can be specified. Depending on the value set in the Minimum Hit Quality field, the resulting number of hits can be lower than this value. 

Figure 11.30. The short report of the spectrum search for the sample Unknown 4 based on the standard algorithm with the following parameters search sensitivity 14, maximum numbers of hits 5, minimum hit quality 300, and no excluded regions.

Parallel searching of Raman and infrared libraries of specfra of unknown substances will first increase the reliability of found and coincided main components and, secondly, permit one to enhance the hit quality of minor components revealed by the spectral subtraction of components found by a complementary method. The value of Raman spectra in such analyses comes not only from its complementarity but also from the sharpness of Raman bands. Such widely used extenders and / or pigments as carbonates, silicates, or sulfates are characterized by very broad infrared bands with overlapping wide spectral ranges, whilst their Raman bands are narrow. 

Aim of the data retrieval stage is to extract all relevant raw data from their respective sources. The user is usually able to execute these workflows as they are and has nothing to care about the technical details of the various data sources. Test results for biological activity can be retrieved in standardized format from the corporate compound database (CDB, cf. Section 13.2.1). Other data, such as the BioProfde (cf. Section 13.3.2), the library information for compounds originating from combinatorial soeening hbraries or hit quality check results have to be retrieved directly from different laboratory information systems or from the CIDB(cf. Section 13.2.2). Once all the relevant data have been retrieved, it is stored conveniently as a set of files. This easily accessible collection serves as foundation for all subsequent analyses. 

Another often used algorithm for simple spectral matching is the vector correlation method. It is similar to the Euclidean distance method but does not require that the spectra be normalized. In this method, each spectrum is centered around the mean response value to calculate the hit quality index. 

Finally, the hit quality index does not provide any absolute measure of the probability that the sample actually is the same as the library sample. The arbitrary scale of the hit quality values (0-1) does not give a very good statistical measure of the similarity of the spectra. In short, using only a single training spectrum to represent all possible samples in the future does not give the analyst any statistical assurance that the spectra are truly the same or different. It provides only a relative measure for all the library samples. For anyone who has tried simple library search techniques for spectrally similar samples, this result is all too obvious. 

AU search algorithms have a goodness-of-fit or hit quality index metric to indicate which spectrum is the best match. In a true Euchdean distance metric, where the spectra are normalized to unity, the metric wiU report a value between zero and 1, where the value of zero represents a perfect match. Commercial software will often scale or adjust this number. For example, if the Euclidean distance is subtracted from 1 and the result multiplied by 1000, a perfect match gives rise to a hit quality index of 1000, and a complete mismatch gives a score of zero. Regardless, the algorithm to compare the spectra is virtually identical only the way the results are reported varies. 

Hit quality indices calculated in this way are independent of the normalization of the spectra, preventing the baseline of a noisy spectrum from being shifted by negative spikes caused either by noise or (for Raman spectra) a cosmic ray event. 

Search systems are powerful tools, but as for most algorithms described in this chapter, care must be taken when they are used. The unknown and the hbrary spectra must both obey Beer s law. Care must be taken that the wavenumber calibration of an instrument is correct as wavenumber shifts may occur between the unknown and the hbrary spectra. The algorithms compensate for spectral shifts to some degree, but they cannot compensate for large shifts. In addition, the spec-troscopist must remember two important caveats (1) a hbrary search cannot identify an unknown unless the unknown is in the hbrary and (2) a high value of the hit quality index is not necessarily an indication of a correct match. The judicious spec-troscopist will visually compare the spectra of the unknown to the best matches to determine if they are indeed identical. In fact, the best test is to perform a spectral subtraction between the unknown and the hbrary match to see if there is a nonzero residual. If the two spectra are not identical, a nonzero subtraction will result. Finally, it should be recognized that spectral similarity does not indicate structural similarity. Certainly, some of the functional groups in structurally similar molecules will be the same, but the overall structure of an unknown and the best matches that result from a search may be quite different. 

Most commercial FTIR software packages come equipped with a library searching capability, or it can be purchased for an additional fee. When a spectral library search is performed, the unknown spectrum is compared to each spectrum in the libraries selected. Thus, if there are 1000 spectra in a library, 1000 comparisons are performed. As a result of each comparison a number called the hit quality index (HQI) is calculated, which is a numerical measure of the similarity between two spectra. In an ideal world the library search will turn up a spectrum similar to the unknown spectrum. It is then assumed the unknown sample is chemically similar to the library sample. This way known spectra can be used to identify unknown spectra. Library searching is so useful in identifying unknowns and interpreting mixture spectra that I encourage all FTIR users to have access to library searching capabilities. 

HQI = Hit Quality Index Libi = Absorbances in library spectrum Unki = Absorbances in unknown spectrum n = Number of data points in the spectrum i = Index of data points... 

When the search algorithm compares the unknown spectrum to each library spectrum, a large number of hit quality indices will be calculated. Sorting through these numbers could be a difficult task, but fortunately FTIR search software programs... 

Hit Quality Index (HQI) In library searching, the number that shows how closely matched a library spectrum is to a sample spectrum. 

Library Searching An automated process where an unknown spectrum is compared to a collection of known spectra kept in a spectral library. The comparison gives a number called the hit quality index, which represents how closely related two spectra are to each other. If a match is of high quality, it may be possible to identify an unknown. 

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