Chemoinformatics for Drug Discovery

Chemoinformatics strategies to improve drug discovery
results

With contributions from leading researchers in academia and the
pharmaceutical industry as well as experts from the software
industry, this book explains how chemoinformatics enhances drug
discovery and pharmaceutical research efforts, describing what
works and what doesn't. Strong emphasis is put on tested and proven
practical applications, with plenty of case studies detailing the
development and implementation of chemoinformatics methods to
support successful drug discovery efforts. Many of these case
studies depict groundbreaking collaborations between academia and
the pharmaceutical industry.

Chemoinformatics for Drug Discovery is logically
organized, offering readers a solid base in methods and models and
advancing to drug discovery applications and the design of
chemoinformatics infrastructures. The book features 15 chapters,
including:

* What are our models really telling us? A practical tutorial on
avoiding common mistakes when building predictive models

* Exploration of structure-activity relationships and transfer of
key elements in lead optimization

* Collaborations between academia and pharma

* Applications of chemoinformatics in pharmaceutical
research--experiences at large international pharmaceutical
companies

* Lessons learned from 30 years of developing successful
integrated chemoinformatic systems

Throughout the book, the authors present chemoinformatics
strategies and methods that have been proven to work in
pharmaceutical research, offering insights culled from their own
investigations. Each chapter is extensively referenced with
citations to original research reports and reviews.

Integrating chemistry, computer science, and drug discovery,
Chemoinformatics for Drug Discovery encapsulates the field
as it stands today and opens the door to further advances.