By Gisbert Schneider
ISBN-10: 3527334610
ISBN-13: 9783527334612
ISBN-10: 3527677011
ISBN-13: 9783527677016
Systematically interpreting present equipment and methods, this prepared reference covers quite a lot of molecular buildings, from organic-chemical medicinal drugs to peptides, Proteins and nucleic acids, in response to rising new drug sessions derived from biomacromolecules.
A chief within the box and one of many pioneers of this younger self-discipline has assembled the following the main widespread specialists from the world over to supply first-hand wisdom. whereas so much in their tools and examples come from the realm of pharmaceutical discovery and improvement, the techniques are both acceptable for chemical probes and diagnostics, insecticides, and the other molecule designed to engage with a organic procedure. a variety of pictures and screenshots illustrate the various examples and approach descriptions.
With its large and balanced assurance, it will be the firststop source not just for medicinal chemists, biochemists and biotechnologists, yet both for bioinformaticians and molecular designers for a few years to come.
From the content:
* Reaction-driven de novo design
* Adaptive equipment in molecular design
* layout of ligands opposed to multitarget profiles
* unfastened strength tools in ligand design
* Fragment-based de novo design
* automatic layout of concentrated and objective family-oriented compound libraries
* Molecular de novo layout via nature-inspired computing
* 3D QSAR methods to de novo drug design
* Bioisosteres in de novo design
* De novo layout of peptides, proteins and nucleic acid buildings, together with RNA aptamers
and many more.
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Additional resources for De novo Molecular Design
Example text
These thermodynamic data suggest that the overall potency that can be obtained through the formation of specific directed interactions is limited. In fact, numerous SAR studies reveal that favorable binding enthalpy is more difficult to achieve for highly potent ligands, which in turn affects the target selectivity of the compounds [49]. 4 Modeling Fitness Landscapes As we will see in more detail in throughout this book, there are numerous metrics and indices that can be used to compute a single value expressing an estimate of the drug- and lead-likeness of bioactive compounds using a pK d estimate (cf.
3 Support Vector Machine The SVM belongs to a class of machine-learning algorithms for classification and regression that are based on the ‘‘kernel trick’’ [81]. The latter is a general method, which allows algorithms that can be formulated in terms of inner products (also dot product, scalar product) to be systematically extended to nonlinear cases. For example, kernel PCA is such a nonlinear descendant of standard linear PCA [82]. 18 Cascaded neural network model that combines three first-stage models, whose output values are fed into a jury network.
They found that ‘‘ . . 6 kcal × mol−1 [ . . ] for the lost translational and rotational degrees of freedom) can only be partly compensated by water–protein interactions, even when they are nearly perfect [ . . ’’ What makes this observation so important for the calculation of protein–ligand interaction energies and protein structure-based de novo drug design is the necessity to consider protein flexibility when generating novel ligands by matching them with binding sites. One can expect immediate progress for receptor-based de novo design from a combination of flexible pocket models with advanced methods for shape and pharmacophore matching (cf.
De novo Molecular Design by Gisbert Schneider
by Edward
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