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We have systematically searched a very large compound collection for other lipid-like inhibitors of mast cell activation.
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This is a very desirable feature when processing very large compound databases.
Results: In this article, we introduce a new algorithm for accelerated similarity searching and clustering of very large compound sets using embedding and indexing (EI) techniques.
This study presents an alternative embedding method that is accurate and robust enough to process very large compound datasets.
The method is also easy to implement and can handle very large compound data sets.
As an example of utility, the visualisation of the chemotype landscape for targets with very large compound sets (e.g. over 10,000) is much easier when the GS ring-type abstractions can be displayed and browsed.
Synchronized PC input, which was effectively achieved with inferior olivary stimulation, resulted in very large compound IPSPs in all of our DCN cells.
Unfortunately, they are often not fast enough forsystematic analyses of very large compound collections with millions of compounds.
However, very large compound microsatellites, containing more than eight cSSRs, can be found in many species (Table 2).
In this study, we have presented EI-Search and EI-Clustering as efficient methods for accelerating structure similarity searches and clustering of very large compound datasets.
Let's change the scenario to one where you need to communicate many different structures, and you want to use these structures within a database containing a very large number of compounds.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com