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200 00$aData mining in drug discovery /$fedited by Re?my D. Hoffmann, Arnaud Gohier, and Pavel Pospisil
210  1$aWeinheim :$cWiley-VCH,$d[2014]
210  4$d©2014
215   $a1 online resource (347 p.)
225 1 $aMethods and principles in medicinal chemistry ;$vvol. 57
300   $aDescription based upon print version of record.
311   $a3-527-32984-6 
311   $a1-299-93919-8 
320   $aIncludes bibliographical references and index.
327   $apart one. Data sources -- part two. Analysis and enrichment -- part three. Applications to polypharmacology -- part four. system biology approaches.
330   $a Written for drug developers rather than computer scientists, this monograph adopts a systematic approach to mining scientifi c data sources, covering all key steps in rational drug discovery, from compound screening to lead compound selection and personalized medicine. Clearly divided into four sections, the first part discusses the different data sources available, both commercial and non-commercial, while the next section looks at the role and value of data mining in drug discovery. The third part compares the most common applications and strategies for polypharmacology, where data mining
410  0$aMethods and principles in medicinal chemistry ;$vv. 57.
606   $aDrug development$xComputer simulation
606   $aData mining
606   $aDrugs$xResearch
615  0$aDrug development$xComputer simulation.
615  0$aData mining.
615  0$aDrugs$xResearch.
676   $a615.102856312
701   $aHoffmann$b Re?my D$0968298
701   $aGohier$b Arnaud$0968299
701   $aPospisil$b Pavel$0968300
801  0$bMiAaPQ
801  1$bMiAaPQ
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906   $aBOOK
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996   $aData mining in drug discovery$92199261
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