By Rémy D. Hoffmann, Arnaud Gohier, Pavel Pospisil, Raimund Mannhold, Hugo Kubinyi, Gerd Folkers
Written for drug builders instead of computing device scientists, this monograph adopts a scientific method of mining scientifi c facts resources, protecting all key steps in rational drug discovery, from compound screening to guide compound choice and custom-made medication. truly divided into 4 sections, the 1st half discusses the various info assets to be had, either advertisement and non-commercial, whereas the following part appears on the function and cost of knowledge mining in drug discovery. The 3rd half compares the most typical functions and techniques for polypharmacology, the place information mining can considerably increase the learn attempt. the ultimate portion of the booklet is dedicated to platforms biology methods for compound trying out.
Throughout the ebook, business and educational drug discovery thoughts are addressed, with participants coming from either parts, allowing an educated selection on whilst and which info mining instruments to exploit for one's personal drug discovery project.
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Extra info for Data Mining in Drug Discovery
6 j 1 Protein Structural Databases in Drug Discovery To stress the quality issue, one can note the recent increase in the number of crystal structures solved at very high resolution: 90% of the 438 structures with a resolution better than 1 A was deposited after year 2000. , alternative conformations of an amino acid side chain) and into the enlarged description of the molecular environment of the biopolymer, that is, of the nonbiopolymer molecules, also named ligands. ), but it can also be biologically relevant molecules (cofactors and prosthetic groups, inhibitors, allosteric modulators, and drugs).
The sc-PDB provides a unique source for such benchmarks since ligand, protein, and active site coordinates have been preprocessed and are ready for automated docking. 0 A) and 47% of scoring accuracy (RMSD of the top-ranked pose <2 A). Along the same lines, we reported the accuracy of four docking algorithms in posing low molecular weight fragments into druggable sc-PDB binding sites and observed that ranking poses by a pure topological scoring function based on protein–ligand interaction ﬁngerprints were much superior to poses by classical energy-based scoring functions .
And Henrick, K. (2008) MSDmotif: exploring protein sites and motifs. BMC Bioinformatics, 9, 312. , and Go, M. : a database for protein– small molecule interactions. ]. J. A. (1998) Databases in protein crystallography. Acta Crystallographica Section D, 54, 1119–1131. , and Preissner, R. (2005) SuperLigands: a database of ligand structures derived from the Protein Data Bank. BMC Bioinformatics, 6, 122. A. (2008) 26 27 28 29 30 31 32 33 34 Binding MOAD, a high-quality protein– ligand database. Nucleic Acids Research, 36, D674–D678.