Database filtering

In addition to the two major categories of LEVS approaches, similarity searching and compound classification, database filtering techniques should also be added to the virtual screening spectrum. Rule-based filters that screen databases for compounds with desired (e.g., drug- or leadlike) or undesired (toxicity and instability) molecular properties are widely used, not to identify individual active compounds but rather to reduce the size of screening databases as much as possible for further studies using more sophisticated methods. 

In figure 5 we see an overview of the system architecture. The Sensor-Network at the elderly side collects raw data that are pulled from the ADL-State Extractor. The ADL-State Extractor abstracts in software terms the sensors and interprets the collected signals to predefined ADL states. These states are time-stamped and pushed to the ADL-State Database Host where they are stored in a database for later process. When it is needed (e.g., on request, or on specific intervals), the ADL-Semantics Extractor pulls the corresponding states from the database, filters and transforms them to an ADL journal that is described in a XML-semantics file. Depending on the configuration, the XML data are pushed to, or pulled by the Presentation Server, which does the final transformation to HTML code. The location of Presentation Server is resolved from a Point to Point Server that redirects the Client requests to the resolved URL. 

Graham, R.W. (2000) FAUNMAP Database Filter effects from field to literature to database to analysis to interpretation. Geological Society of America, Abstracts with Programs, 109 A131. 

Database searching does not ordinarily identify extensively post-translationally modified proteins or sequence variants. When this problem occurs, high-quality MS/MS spectra can return low-confidence matches. Those spectra can then be searched using an error-tolerant database search routine, which considers a large number of unsuspected modifications and primary sequence variations. However, this approach is very computationally expensive. InsPecT is a program that implements an efficient algorithm to identify post-translational modifications by using database filters. ... 

The steps in developing such a database are (1) collection of machine and process data and (2) database setup. Input requirements of the software are machine and process specifications, analysis parameters, data filters, alert/alarm limits, and a variety of other parameters used to automate the data-acquisition process. 

The key elements of database setup discussed in this section are analysis parameter sets, data filters (i.e., band-widths, averaging, and weighting), limits for alerts and alarms, and data-acquisition routes. 

The 2 pharmacophore models were used to search a database of about 1.8 million compounds assembled from 12 commercial databases. Hypothesis 1 returned approximately 20,000 hits, which were aggressively filtered using the Catalyst scoring function, lack of hit to a hERG pharmacophore, clogP <5, fewer than five donors and ten acceptors. Fifteen compounds remained and were available for... 

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