+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)


Molecular Design. Concepts and Applications

  • ID: 2179678
  • Book
  • 277 Pages
  • John Wiley and Sons Ltd
1 of 3
Molecular Design Provides an easy–to–read introduction to the principles and concepts of computer–assisted drug discovery. Written by leading experts in the field, this book is a must–have for students of biological and chemical sciences and for researchers working in drug discovery. Emphasis is on design techniques complemented by carefully selected practical examples and case studies.

Richly illustrated, the beginner is guided from first principles to state–of–the–art techniques in virtual screening and molecular design.

Thus, this monograph is considered to be of utmost importance, not only for the beginner and the experienced modeler, but also for all interested medicinal chemists, biochemists and biologists.

Note: Product cover images may vary from those shown
2 of 3
Molecular Objects and Design Objectives

– Molecular geometry and surface

– Molecular properties

– The concept of drug–likeness

– Representing molecules as strings

Receptor–Ligand Interaction

– Thermodynamics of protein–ligand interaction

– QSAR: estimating quantitative structure–activity relationships

– The biophore concept

– Pharmacophores

Creating the Design

– Rational drug design

– Ligand–based design of compound libraries

– Transition state analogs

– de novo design

Virtual Screening

– Similarity searching

– Pharmacophore–based virtual screening

– Molecular docking and scoring

– Structure–based vs. ligand–based design

– Case study 1: design of Kv1.5 ion channel modulators

– Case study 2: virtual screening of a natural–product derived combinatorial library for novel 5–lipoxygenase inhibitors

– Case study 3: scaffold de novo design for cannabinoid–1 (CB–1) receptor ligands

Secondary design constraints and machine learning

– Introduction to Pharmacokinetics

– Prodrugs and bioisosters

– Machine learning methods

– Case study 1: predicting cross–activities of allosteric modulators of metabotropic glutamate receptors (mGluR)

– Case study 2: dopamine D3 antagonists and ACE inhbitors

Note: Product cover images may vary from those shown
3 of 3


4 of 3
Gisbert Schneider
Karl–Heinz Baringhaus
Note: Product cover images may vary from those shown