“Data Mining in Drug Discovery. Methods and Principles in Medicinal Chemistry”

From tools to applications, this is a systematic approach to mining chemical, biological and literature data sources for drug discovery purposes. Throughout, industrial and academic drug discovery strategies are addressed, with contributors coming from both areas.

Key Topics Covered:

Introduction

DATA SOURCES

- Databases of Organic Compounds

- Protein Databases

- Databases for Protein-Protein Interactions

- Combining Chemistry and Biology

- Ontologies and Data Mining

Data Mining Tools:

- Chemistry and Biology 1: Knime

- Chemistry and Biology 2: Accelrys

- Chemistry and Biology 3: Inforsense

- Chemistry and Biology 4: Tibco/Spotfire

- Statistical Tools and Open Source Codes for Medicinal Chemistry: R, Weka, Rapid Miner

- Discovery/Polypharmacology 1: Tripos

- Discovery/Polypharmacology 2: Ariana Pharma

Applications:

- Data Mining and Screening

- Exploiting Systems Chemical Biology to Predict and Understand (Un)desired Drug Effects

- Micro-Arrays

- High Throughput Computational Biology

- Translational Medicine and Personalized Medicine

Authors:

Rémy D. Hoffmann.

Arnaud Gohier.

Raimund Mannhold.

Hugo Kubinyi.

Gerd Folkers.

For more information visit http://www.researchandmarkets.com/research/wtbgfm/data_mining_in_dru

Source: John Wiley and Sons Ltd