Pharmaceutical Data Mining

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.

Leading experts illustrate how sophisticated computational datamining techniques can impact contemporary drug discovery anddevelopmentIn 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 approachesIn 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 sciencecomputational 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.

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.