Chemoinformatics: Theory, Practice, & Products by Barry A. Bunin, Brian Siesel, Guillermo Morales, Jürgen

By Barry A. Bunin, Brian Siesel, Guillermo Morales, Jürgen Bajorath

Chemoinformatics is using details expertise within the acquisition, research and administration of knowledge and knowledge in relation to chemicals and their houses. the aim of this ebook is to supply computational scientists, medicinal chemists and biologists with entire useful details and underlying thought on the subject of sleek Chemoinformatics and similar drug discovery informatics applied sciences. this is often a necessary guide for picking the suitable Chemoinformatics approach or expertise to take advantage of.

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2 Compound selection for medicinal chemistry. Going beyond data analysis and management, chemoinformatics can form viable interfaces with medicinal chemistry and biological screening, as already mentioned before. 25. 43 User interface of a relational database ranging from the selection of compounds that are suitable for medicinal chemistry applications or the design of focused libraries to computational lead optimization efforts. Michael S. Lajiness and colleagues, then at Pharmacia, have reported an interesting case study focusing on compound selection (Lajiness and Shanmugasundaram, 2004).

Passive absorption, the ability of molecules to pass through membranes, provides another example for a physico-chemical property that can be well predicted using a simple filter function. Two descriptors accounting for polar molecular surface area and log P(o/w), the octanol/water partition coefficient, are generally sufficient for accurate predictions and compounds with less than 140Å2 polar surface area and a log P(o/w) between zero and four usually have favorable absorption characteristics (Egan et al.

B-score (BS) summarizing known potency, selectivity, and toxicity of library compounds that had already been assayed in-house, D-score (DS) providing a measure of dissimilarity of a test compound from others in the library or selection set, and S-scores (SS) reporting the similarity of a compound to a selected template molecule (if available), for example, a known lead or drug candidate. QS was implemented as a combination of different descriptor contributions: QS ϭ ͚ n iϭ1wiS(di) n iϭ1wi ͚ where wi is the weight of S(di), which represents the (regularized) score for the ith of n chosen descriptors.

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