+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)


Biomolecular Simulations in Drug Discovery. Methods and Principles in Medicinal Chemistry

  • ID: 4541106
  • Book
  • November 2018
  • 368 Pages
  • John Wiley and Sons Ltd
A timely and topical survey of modern simulation tools and their applications in real–life drug discovery, allowing for better and quicker results in structure–based drug design.

The first part of this practical guide for industry professionals describes common tools used in the biomolecular simulation of drugs and their targets. A critical analysis of the accuracy of the predictions, the integration of modeling with other experimental data combined with numerous case studies from different therapeutic fields enable users to quickly adopt these new methods for their current projects. The second part then shows how these tools can be applied to drug discovery and development projects. Modeling experts from the pharmaceutical industry and from leading academic institutions present real–life examples for important target classes such as GPCRs, kinases and amyloids as well as for common challenges in structure–based drug discovery.

With its inclusion of novel methods and strategies for the modeling of drug–target interactions in the framework of real–life drug discovery and development, this application–oriented reference is tailor–made for medicinal chemists and those working in the pharmaceutical industry.
Note: Product cover images may vary from those shown
PART 1: Principles

Predictive power of biomolecular simulations

Docking by MD

Ligand force fields


Waterswap approach

Binding free energy predictions based on implicit solvent models

Accuracy of biomolecular simulations

Entropy/enthalpy compensation

Induced fit

Combination of simulations with experiment

Analysis of simulation data

PART 2: Applications

Molecular dynamics applications in GPCR drug design

Metadynamics–based docking, metadynamics in development of SSR128129E

Protein kinases

E2 modelling

polyGlu amyloids

Inorganic HIV protease inhibitors

Molecular dynamics simulation in virtual screening


Markov models

Note: Product cover images may vary from those shown
Francesco L. Gervasio
Vojtech Spiwok
Raimund Mannhold
Helmut Buschmann
Jörg Holenz
Note: Product cover images may vary from those shown