Konstantin V. Balakin

Pharmaceutical Data Mining (eBook, PDF)

Approaches and Applications for Drug Discovery

• Format: PDF

Produktbeschreibung

Leading experts illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development In the era of post-genomic drug development, extracting and applying knowledge from chemical, biological, and clinical data is one of the greatest challenges facing the pharmaceutical industry. Pharmaceutical Data Mining brings together contributions from leading academic and industrial scientists, who address both the implementation of new data mining technologies and application issues in the industry. This accessible, comprehensive collection discusses important theoretical and practical aspects of pharmaceutical data mining, focusing on diverse approaches for drug discovery--including chemogenomics, toxicogenomics, and individual drug response prediction. The five main sections of this volume cover: * A general overview of the discipline, from its foundations to contemporary industrial applications * Chemoinformatics-based applications * Bioinformatics-based applications * Data mining methods in clinical development * Data mining algorithms, technologies, and software tools, with emphasis on advanced algorithms and software that are currently used in the industry or represent promising approaches In one concentrated reference, Pharmaceutical Data Mining reveals the role and possibilities of these sophisticated techniques in contemporary drug discovery and development. It is ideal for graduate-level courses covering pharmaceutical science, computational chemistry, and bioinformatics. In addition, it provides insight to pharmaceutical scientists, principal investigators, principal scientists, research directors, and all scientists working in the field of drug discovery and development and associated industries.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 584
• Erscheinungstermin: 25. November 2009
• Englisch
• ISBN-13: 9780470567616
• Artikelnr.: 37297257

Autorenporträt

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.

Inhaltsangabe

Preface. Acknowledgments. Contributors. PART I: DATA MINING IN THE
PHARMACEUTICAL INDUSTRY: A GENERAL OVERVIEW. 1 A History of the development
of Data Mining in Pharmaceutical Research ( David J. Livingstone and John
Bradshaw). 2 Drug Gold and Data Dragons: Myths and Realities of Data Mining
in the Pharmaceutical Industry (Barry Robson and Andy Vaithiligam). 3
Application of Data Mining Algorithms in Pharmaceutical Research and
Development (Konstantin V. Balakin and Nikolay P. Savchuk). PART II:
CHEMOINFORMATICS-BASED APPLICATIONS. 4 Data Mining Approaches for Compound
Selection and Iterative Screening (Martin Vogt and Jurgen Bajorath). 5
Prediction of Toxic Effects of Pharmaceutical Agents (Andreas Maunz and
Christoph Helma). 6 Chemogenomics-Based Design of GPCR-Targeted Libraries
Using Data Mining Techniques (Konstantin V. Balakin and Elena V. Bovina). 7
Mining High-Throughput Screening Data by Novel Knowledge-Based Optimization
Analysis (S. Frank Yan, Frederick J. King, Sumit K. Chanda, Jeremy S.
Caldwell, Elizabeth A. Winzeler, and Yingyao Zhou). PART III:
BIOINFORMATICS-BASED APPLICATIONS. 8 Mining DNA Microarray Gene Expression
Data (Paolo Magni). 9 Bioinformatics Approaches for Analysis of
Protein-Ligand Interactions (Munazah Andrabi, Chioko Nagao, Kenji
Mizuguchi, and Shandar Ahmad). 10 Analysis of Toxicogenomic Databases (Lyle
D. Burgoon). 11 Bridging the Pharmaceutical Shortfall: Informatics
Approaches to 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 Andrew Bate). 13 Data Mining Methods as Tools for Predicting Individual
Drug Response (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 Data Mining (Igor V. Pletnev, Yan A.
Ivanenkov, and Alexey V. Tarasov). 16 Advanced Artificial Intelligence
Methods Used in the Design of 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.