Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery (Hardcover)

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.

領先的專家們展示了複雜的計算數據挖掘技術如何影響當代藥物發現和開發。

在後基因組藥物開發時代，從化學、生物和臨床數據中提取和應用知識是制藥行業面臨的最大挑戰之一。《藥物數據挖掘》匯集了來自領先的學術和工業科學家的貢獻，他們討論了新數據挖掘技術的實施和行業應用問題。這本易於理解、全面的專著討論了藥物數據挖掘的重要理論和實踐方面，重點介紹了藥物發現的多種方法，包括化學基因組學、毒理基因組學和個體藥物反應預測。本書的五個主要部分包括：

- 這一學科的概述，從基礎到當代工業應用
- 基於化學信息學的應用
- 基於生物信息學的應用
- 臨床開發中的數據挖掘方法
- 數據挖掘算法、技術和軟件工具，重點介紹目前在行業中使用的先進算法和軟件，或者具有潛在應用價值的方法

《藥物數據挖掘》一書集中展示了這些複雜技術在當代藥物發現和開發中的作用和可能性。它非常適合研究生課程，涵蓋藥物科學、計算化學和生物信息學。此外，它還為從事藥物發現和開發以及相關行業的制藥科學家、首席調查員、首席科學家、研究總監和所有從事該領域工作的科學家提供了深入的見解。