Decoy database

Rigorously validated the in silico generated MS/MS analysis. The group performed evaluations to detect false positives and false negatives, using decoy database searches and MS/MS analysis of authentic lipid standards measured in-house and from the literature. The search parameters and detailed statistics are available at the website given above. 

Ithough knowledge-based potentials are most popular, it is also possible to use other types potential function. Some of these are more firmly rooted in the fundamental physics of iteratomic interactions whereas others do not necessarily have any physical interpretation all but are able to discriminate the correct fold from decoy structures. These decoy ructures are generated so as to satisfy the basic principles of protein structure such as a ose-packed, hydrophobic core [Park and Levitt 1996]. The fold library is also clearly nportant in threading. For practical purposes the library should obviously not be too irge, but it should be as representative of the different protein folds as possible. To erive a fold database one would typically first use a relatively fast sequence comparison lethod in conjunction with cluster analysis to identify families of homologues, which are ssumed to have the same fold. A sequence identity threshold of about 30% is commonly... 

A useful pharmacophore model must be able to correctly classify - and in the case of quantitative models, correctly predict the affinity of - a so-called test or validation set, which consists of active compounds that were not used during the generation of the model. Often the ability of the model to retrieve known actives from a larger drug-like database is also assayed. It has been confirmed by several studies that the characteristics of the known inactives or decoys chosen for VS assessments have a significant impact on the enrichment of VS approaches. ... 

The general aim of VS methods is to retrieve from a molecular database a fraction of true positives that is significantly larger than that of a random compound selection. If a VS method selects n molecules from a database with N entries, the selected hit list consists of active compounds (true positive compounds, TP) and decoys (false positive compounds, FP). Active molecules that are not retrieved by the VS method are defined false negatives FN), whereas the unselected database decoys represent the true negatives TN) (Figure 3.3). 

Obtaining structural data of biologically tested inactive molecules is even more challenging. A limited number of inactive molecules have been published in literature. Thus, some computational chemistry groups used compounds structurally similar to actives but experimentally not tested for biological activity as putative inactive molecules or the so-called decoys. A prominent validation database... 

To build this directory, 2950 active molecules for 40 proteins were derived from literature. For each of the 2950 actives, 36 decoys were added to the database. For this purpose, a total of 95 316 decoys were obtained from a set of drug-like commercially available compounds [62]. However, Irwin [63] reported several benchmark biases (e.g., analogue bias) discovered in the DUD database. Moreover, this fact cannot be denied that some of the commercial compounds used as decoys have biological activity for the corresponding target protein. 

These conclusions are also supported by the presented examples, but some additional trends should be pointed out. Effective virtual screening today includes a multitude of approaches, often applied in a sequential manner or integrated within a flexible workflow. There is no unique protocol for every virtual screening task -every problem is unique and requires careful validation, filtering, and parameterization in the beginning to result in a protocol with the potential to yield valuable hit lists after productive application to large databases. Extensive validation based on sample databases of known actives and decoy molecules is essential. 

DOCKGROUND http / / dockground.bioinformatics. ku.edu/index.shtml The DOCKGROUND resource implements a comprehensive database of cocrystalbzed (bound-bound) protein-protein complexes, unbound (experimental and simulated) protein-protein complexes, modeled protein-protein complexes and systematic sets of docking decoys... 

We have built one-class models for data taken from the DUD database [47], which contains the stmctures of active ligands for different biological targets, as well as the stmctures of corresponding decoys. It is worth noting that the latter were used only for assessing the statistical characteristics of classification models and were not involved in their constmction. In particular, decoys were used for determining the TN and FP models constmcted with the use of active compounds. 

Successful VS rehes on the abihty to discriminate between active and inactive compounds in order to provide a set of compounds for experimental screening that is highly emiched in active molecules [93]. Sets of known active and inactive compounds are needed for the assessment of VS approaches. Decoys are molecules that are presumed to be inactive against a target, which can be used when too few inactive compounds are available for such testing [94]. Many metrics are currently used to quantify the effectiveness of a VS [95]. The enrichment factor (EF) represents one of the most prominent metrics in VS. EF measures how maity more active compounds are found within a defined early recognition fraction of the ordered list relative to a random distribution. Sensitivity and specificity are also descriptors that assess the enrichment of active molecules from a database. Sensitivity (Se, or true positive rate) describes the ratio of the number of active molecules found by the VS method to the number of all active compounds in the database. Specificity (Sp, or true negative rate) represents the ratio of the number of inactive compounds that were not selected by the VS protocol to the number of all inactive molecules included in the database [93]. 

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