Chemoinformatics

In the literature, several terms are used synonymously to name the topic of this book: chem-, chemi-, or chemo-informatics. A widely recognized de- nition of this discipline is the one by Frank Brown from 1998 (1) who defined chemoinformatics as the combination of all the information resources that a scientist needs to optimize the properties of a ligand to become a drug. In Brown's definition, two aspects play a fundamentally important role: de- sion support by computational means and drug discovery, which distinguishes it from the term chemical informatics that was introduced at least ten years earlier and described as the application of information technology to ch- istry (not with a specific focus on drug discovery). In addition, there is of course chemometrics, which is generally understood as the application of statistical methods to chemical data and the derivation of relevant statistical models and descriptors (2). The pharmaceutical focus of many developments and efforts in this areaand the current popularity of gene-to-drug or si- lar paradigmsis further reflected by the recent introduction of such terms as discovery informatics (3), which takes into account that gaining kno- edge from chemical data alone is not sufficient to be ultimately successful in drug discovery. Such insights are well in accord with other views that the boundaries between bio- and chemoinformatics are fluid and that these d- ciplines should be closely combined or merged to significantly impact b- technology or pharmaceutical research (4).

Includes supplementary material: sn.pub/extras

Klappentext
In the post-genomic era, the role informatics technologies plays in chemical and pharmaceutical research has become increasingly important in data management and lead-compound identification. In Chemoinformatics: Concepts, Methods, and Tools for Drug Discovery, well-recognized pioneers and investigators from diverse professional environments survey the key concepts in the field, describe cutting-edge methods, and provide exemplary pharmaceutical applications. The authors explain the theory behind the crucial concepts of molecular similarity and diversity, describe the challenging efforts to use chemoinformatics approaches to virtual and high-throughput screening, and illuminate the latest developments in multidimensional QSAR analysis. Other topics of interest include the use of partitioning algorithms and classification methods for analyzing large compound databases, screening sets, and virtual screening for active molecules; different approaches to target class-specific library design; the generation of novel classes of molecular descriptors; and Web-based tools for chemical database access and management. Also presented are different methods for describing molecular chirality and conformational parameters and for predicting the drug-like character and basic ADME properties of compounds based on modeling their putative interactions with cytochrome P450 isoforms.
State-of-the-art and user-friendly, Chemoinformatics: Concepts, Methods, and Tools for Drug Discovery illuminates the conceptual and methodological diversity of this rapidly evolving field and offers instructive examples of cutting-edge applications in the drug discovery process.

Inhalt
Molecular Similarity Measures.- Evaluation of Molecular Similarity and Molecular Diversity Methods Using Biological Activity Data.- A Web-Based Chemoinformatics System for Drug Discovery.- Application of Chemoinformatics to High-Throughput Screening.- Strategies for the Identification and Generation of Informative Compound Sets.- Methods for Applying the Quantitative Structure-Activity Relationship Paradigm.- 3D-LogP.- Derivation and Applications of Molecular Descriptors Based on Approximate Surface Area.- Cell-Based Partitioning.- Partitioning in Binary-Transformed Chemical Descriptor Spaces.- Comparison of Methods Based on Diversity and Similarity for Molecule Selection and the Analysis of Drug Discovery Data.- Using Recursive Partitioning Analysis to Evaluate Compound Selection Methods.- Designing Combinatorial Libraries Optimized on Multiple Objectives.- Approaches to Target Class Combinatorial Library Design.- Simulated Annealing.- Genetic Algorithms for Classification of Olfactory Stimulants.- How to Describe Chirality and Conformational Flexibility.- Novel Scoring Methods in Virtual Ligand Screening.- Prediction of Drug-Like Molecular Properties.