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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 can substantially enhance the research effort. The final section of the book is devoted to systems biology approaches for compound testing.
Throughout the book, industrial and academic drug discovery strategies are addressed, with contributors coming from both areas, enabling an informed decision on when and which data mining tools to use for one's own drug discovery project.
Table of Contents
Databases of Organic Compounds
Databases for Protein-Protein Interactions
Combining Chemistry and Biology
Ontologies and Data Mining
DATA MINING TOOLS
Chemistry and Biology 1: Knime
Chemistry and Biology 2: Accelrys
Chemistry and Biology 3: Inforsense
Chemistry and Biology 4: Tibco/Spotfire
Statistical Tools and Open Source Codes for Medicinal Chemistry: R, Weka, Rapid Miner
Discovery/Polypharmacology 1: Tripos
Discovery/Polypharmacology 2: Ariana Pharma
Data Mining and Screening
Exploiting Systems Chemical Biology to Predict and Understand (Un)desired Drug Effects
High Throughput Computational Biology
Translational Medicine and Personalized Medicine