Pharmaceutical Data Mining
Approaches and Applications for Drug Discovery
Published on 25. November 2009
528 pages
978-0-470-56761-6 (ISBN)
Description
Leading experts illustrate how sophisticated computational datamining techniques can impact contemporary drug discovery anddevelopment
In the era of post-genomic drug development, extracting andapplying knowledge from chemical, biological, and clinical data isone of the greatest challenges facing the pharmaceutical industry.Pharmaceutical Data Mining brings together contributions fromleading academic and industrial scientists, who address both theimplementation of new data mining technologies and applicationissues in the industry. This accessible, comprehensive collectiondiscusses important theoretical and practical aspects ofpharmaceutical data mining, focusing on diverse approaches for drugdiscovery--including chemogenomics, toxicogenomics, andindividual drug response prediction. The five main sections of thisvolume cover:
* A general overview of the discipline, from its foundations tocontemporary industrial applications
* Chemoinformatics-based applications
* Bioinformatics-based applications
* Data mining methods in clinical development
* Data mining algorithms, technologies, and software tools, withemphasis on advanced algorithms and software that are currentlyused in the industry or represent promising approaches
In one concentrated reference, Pharmaceutical Data Miningreveals the role and possibilities of these sophisticatedtechniques in contemporary drug discovery and development. It isideal for graduate-level courses covering pharmaceutical science,computational chemistry, and bioinformatics. In addition, itprovides insight to pharmaceutical scientists, principalinvestigators, principal scientists, research directors, and allscientists working in the field of drug discovery and developmentand associated industries.
Reviews / Votes
"Its strength is that it gives beginners a good impression of ourcontemporary data jungle." (ChemMedChem, 2010)More details
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Book
01/2010
Wiley
€166.50
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Konstantin V. Balakin
Pharmaceutical Data Mining - Approaches and tions for Drug Discovery
Software
12/2009
Wiley
€160.17
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Persons
Konstantin V. Balakin is Head of the Laboratory of Information Technology in Medicinal Chemistry at the Institute of Physiologically Active Compounds at the Russian Academy of Sciences. He is also Director of the scientific consortium "Orchemed" (Organic Chemistry and Medicine), which currently includes 11 Russian academic institutes working in the field of organic, medicinal and biological chemistry, and drug discovery. Previously, he was Head of the Computational Chemistry Department at ChemDiv, Inc.¿Dr. Balakin¿is the author or coauthor of more than 90 peer reviewed research articles, reviews, and book chapters. He is the principal developer of the SmartMining and InformaGenesis software tools, which are special programs for pharmaceutical multivariate data mining.
Content
Preface.
Acknowledgments.
Contributors.
PART I: DATA MINING IN THE PHARMACEUTICAL INDUSTRY: A GENERALOVERVIEW.
1 A History of the development of Data Mining in PharmaceuticalResearch ( David J. Livingstone and John Bradshaw).
2 Drug Gold and Data Dragons: Myths and Realities of Data Miningin the Pharmaceutical Industry (Barry Robson and AndyVaithiligam).
3 Application of Data Mining Algorithms in PharmaceuticalResearch and Development (Konstantin V. Balakin and Nikolay P.Savchuk).
PART II: CHEMOINFORMATICS-BASED APPLICATIONS.
4 Data Mining Approaches for Compound Selection and IterativeScreening (Martin Vogt and Jurgen Bajorath).
5 Prediction of Toxic Effects of Pharmaceutical Agents (AndreasMaunz and Christoph Helma).
6 Chemogenomics-Based Design of GPCR-Targeted Libraries UsingData Mining Techniques (Konstantin V. Balakin and Elena V.Bovina).
7 Mining High-Throughput Screening Data by Novel Knowledge-BasedOptimization Analysis (S. Frank Yan, Frederick J. King, Sumit K.Chanda, Jeremy S. Caldwell, Elizabeth A. Winzeler, and YingyaoZhou).
PART III: BIOINFORMATICS-BASED APPLICATIONS.
8 Mining DNA Microarray Gene Expression Data (Paolo Magni).
9 Bioinformatics Approaches for Analysis of Protein-LigandInteractions (Munazah Andrabi, Chioko Nagao, Kenji Mizuguchi, andShandar Ahmad).
10 Analysis of Toxicogenomic Databases (Lyle D. Burgoon).
11 Bridging the Pharmaceutical Shortfall: Informatics Approachesto the Discovery of Vaccines, Antigens, Epitopes, and Adjuvants(Matthew N. Davies and Darren R. Flower).
PART IV: DATA MINING METHODS IN CLINICAL DEVELOPMENT.
12 Data Mining in Pharmacovigilance (Manfred Hauben and AndrewBate).
13 Data Mining Methods as Tools for Predicting Individual DrugResponse (Audrey Sabbagh and Pierre Darlu).
14 Data Mining Methods in Pharmaceutical Formulation (Raymond C.Rowe and Elizabeth A Colbourn).
PART V: DATA MINING ALGORITHMS AND TECHNOLOGIES.
15 Dimensionality Reduction Techniques for Pharmaceutical DataMining (Igor V. Pletnev, Yan A. Ivanenkov, and Alexey V.Tarasov).
16 Advanced Artificial Intelligence Methods Used in the Designof Pharmaceutical Agents (Yan A. Ivanenkov and Ludmila M.Khandarova).
17 Databases for Chemical and Biological Information (Tudor I.Oprea, Liliana Ostopovici-Halip, and Ramona Rad-Curpan).
18 Mining Chemical Structural Information from the Literature(Debra L. Banville).
Index.
