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From the Publisher"This book provides a brilliant first access to the interdisciplinary field of molecular design. ...a "must have"."
Journal of Chemical Information and Modeling
This first introductory-level textbook on the design of small molecules is written with the first-time user in mind. Aimed at students and scientists alike, it uses computer-based methods to design and analyze such small molecules as drugs, enzyme inhibitors, probes and markers for biomolecules. Both authors have extensive practical experience of modeling and design and share their knowledge of what can and cannot be done with computer-assisted design.
Divided into four sections, the book begins with a look at molecular objects and design objectives, including molecular geometry, properties, recognition and dynamics. Two further sections deal with virtual synthesis and screening, while the final section covers navigation in chemical space.
The result is a textbook that takes the modeler one step further, to the de novo design of functional molecules. With its study questions at the end of each learning unit, this is equally suitable for teaching and self-learning.
Molecular Objects and Design Objectives
- Molecular geometry and surface
- Molecular properties
- The concept of drug-likeness
- Representing molecules as strings
- Thermodynamics of protein-ligand interaction
- QSAR: estimating quantitative structure-activity relationships
- The biophore concept
Creating the Design
- Rational drug design
- Ligand-based design of compound libraries
- Transition state analogs
- de novo design
- 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