Pharmaceutical Data Mining Approaches and Applications for Drug Discovery
by Balakin, Konstantin V.; Ekins, SeanEdition: 1st
Format: Hardcover
Pub. Date: 2009-12-21
Publisher(s): Wiley
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Summary
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.
Author Biography
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 Science. 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 ht 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 development of the SmartMining and InformaGenesis software, tools, which are special programs for pharmaceutical multivariate data mining.
Table of Contents
| Preface | p. ix |
| Acknowledgments | p. xi |
| Contributors | p. xiii |
| Data Mining in the Pharmaceutical Industry: A general overview | p. 1 |
| A History of the Development of Data Mining in Pharmaceutical Research | p. 3 |
| Drug Gold and Data Dragons: Myths and Realities of Data Mining in the Pharmaceutical Industry | p. 25 |
| Application of Data Mining Algorithms in Pharmaceutical Research and Development | p. 87 |
| Chemoinformatics-based Applications | p. 113 |
| Data Mining Approaches for Compound Selection and Iterative Screening | p. 115 |
| Prediction of Toxic Effects of Pharmaceutical Agents | p. 145 |
| Chemogenomics-Based Design of GPCR-Targeted Libraries Using Data Mining Techniques | p. 175 |
| Mining High-Throughput Screening Data by Novel Knowledge-Based Optimization Analysis | p. 205 |
| Bioinformatics-based Applications | p. 235 |
| Mining DNA Microarray Gene Expression Data | p. 237 |
| Bioinformatics Approaches for Analysis of Protein-Ligand Interactions | p. 267 |
| Analysis of Toxicogenomic Databases | p. 301 |
| Bridging the Pharmaceutical Shortfall: Informatics Approaches to the Discovery of Vaccines, Antigens, Epitopes, and Adjuvants | p. 317 |
| Data Mining methods in Clinical Development | p. 339 |
| Data Mining in Pharmacovigilance | p. 341 |
| Data Mining Methods as Tools for Predicting Individual Drug Response | p. 379 |
| Data Mining Methods in Pharmaceutical Formulation | p. 401 |
| Data Mining Algorithms and Technologies | p. 423 |
| Dimensionality Reduction Techniques for Pharmaceutical Data Mining | p. 425 |
| Advanced Artificial Intelligence Methods Used in the Design of Pharmaceutical Agents | p. 457 |
| Databases for Chemical and Biological Information | p. 491 |
| Mining Chemical Structural Information from the Literature | p. 521 |
| Index | p. 545 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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