By Rémy D. Hoffmann, Arnaud Gohier, Pavel Pospisil, Raimund Mannhold, Hugo Kubinyi, Gerd Folkers
Written for drug builders instead of machine scientists, this monograph adopts a scientific method of mining scientifi c information resources, overlaying all key steps in rational drug discovery, from compound screening to steer compound choice and customized medication. truly divided into 4 sections, the 1st half discusses different information assets to be had, either advertisement and non-commercial, whereas the following part appears on the function and price of information mining in drug discovery. The 3rd half compares the most typical functions and methods for polypharmacology, the place info mining can considerably improve the learn attempt. the ultimate portion of the e-book is dedicated to platforms biology methods for compound trying out.
Throughout the booklet, business and educational drug discovery techniques are addressed, with individuals coming from either components, permitting an educated determination on whilst and which facts mining instruments to exploit for one's personal drug discovery project.
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Extra resources for Data Mining in Drug Discovery, 1st Edition
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For example, if a cell-based screen reveals that a new compound inhibits apoptosis, then one might seek similar compounds that bind apoptosis-related proteins and thus hypothesize that the new compound also binds one of these targets. Similarly, if one is prioritizing several lead compounds for further development, the observation that one lead is similar to a published compound known to bind a different target might lead one to reduce its priority to minimize off-target effects. In another scenario, marking all the proteins in a deﬁned signaling pathway according to which ones are already targeted by FDA-approved drugs might lead to suggestions for a multidrug therapy to maximally suppress signaling.