Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery

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 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, form its foundations to contemporary industrial applications

Chemoinformatics-based applications

Bioinformatic-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 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.

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.