Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery (Wiley Series on Technologies for the Pharmaceutical Industry)

Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery (Wiley Series on Technologies for the Pharmaceutical Industry)

By: Konstantin V. Balakin (author), Sean Ekins (series_editor)Hardback

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Extracting and applying knowledge from chemical, biological, and clinical data is one of the biggest problems for the pharmaceutical industry. Focusing on diverse data mining approaches for drug discovery, including chemogenomics, toxicogenomics, and individual drug response prediction, Pharmaceutical Data Mining links theory to applications to illustrate how sophisticated computational data mining techniques can impact contemporary drug discovery and development. The book is of vital interest to pharmaceutical scientists, principal investigators and scientists, research directors, industrial and academic research libraries, and graduate students.

About Author

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.


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.

Product Details

  • ISBN13: 9780470196083
  • Format: Hardback
  • Number Of Pages: 584
  • ID: 9780470196083
  • weight: 928
  • ISBN10: 0470196084

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