By Barry A. Bunin, Brian Siesel, Guillermo Morales, Jürgen Bajorath
Chemoinformatics is using details know-how within the acquisition, research and administration of information and knowledge when it comes to chemical substances and their homes. the aim of this publication is to supply computational scientists, medicinal chemists and biologists with entire useful info and underlying idea in terms of glossy Chemoinformatics and similar drug discovery informatics applied sciences. this can be an important instruction manual for picking the correct Chemoinformatics technique or know-how to take advantage of.
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Extra info for Chemoinformatics: Theory, Practice, & Products
28. Here a collaborator had already screened ~300,000 compounds prior to VS analysis, but could not identify any novel hits. Based on compounds patented by another pharmaceutical company, VS analysis was carried out following the unsuccessful HTS campaign. 5 million virtual database compounds, only 30 candidates were selected and only 16 of these molecules could actually be acquired from chemical vendors. However, one of these 16 compounds that were assayed turned out to be a novel hit and was structurally distinct from the search templates.
More complex reaction schemes and large reagent numbers can easily result in literally billions of potential products. Even full enumeration of moderately sized libraries is usually not meaningful because many potential products are likely to be very similar to each other and provide redundant chemical information. In drug discovery, the information content of large chemical libraries is a particularly critical issue because these libraries must be screened in order to identify novel active compounds, which can become a daunting and expensive task, despite the availability of high-throughput screening technology.
If the QSAR model suggests, for example, that increasing the charge of test compounds might improve potency, grid energy contours might suggest where a positively or negatively charged group could be placed in these compounds. 4 4D-QSAR. The 4D-QSAR approach (Hopfinger et al. 1997) was designed to address the major problem of 3D-QSAR, the accurate prediction of bioactive compound conformations. This has been successfully done by calculating conformational ensembles of test compounds, rather than single conformations, which adds conformational space as a fourth dimension to QSAR.
Chemoinformatics: Theory, Practice, & Products by Barry A. Bunin, Brian Siesel, Guillermo Morales, Jürgen Bajorath