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Comprehensive resource discussing the future of data-driven drug
discovery and the growing number of open-source databases With an
overview of 90 freely accessible databases and datasets on all
aspects of drug design, development, and discovery, Open Access
Databases and Datasets for Drug Discovery is a comprehensive guide
to the vast amount of "free data" available to today's
pharmaceutical researchers. Throughout the text, the applicability
of open-source data for drug discovery and development is analyzed,
and their usefulness in comparison with commercially available
tools is evaluated. The most relevant databases for small
molecules, drugs and druglike substances, ligand design, protein 3D
structures (both experimental and calculated), and human drug
targets are described in depth, including practical examples of how
to access and work with the data. The first part is focused on
databases for small molecules, followed by databases for
macromolecular targets and diseases. The final part shows how to
integrate various open-source tools into the academic and
industrial drug discovery and development process. Contributed to
and edited by experts with long-time experience in the field, Open
Access Databases and Datasets for Drug Discovery includes
information on: A comprehensive listing of open access databases
and datasets for computer-aided drug design PubChem as a chemical
database for drug discovery, DrugBank Online, and bioisosteric
replacement for drug discovery supported by the SwissBioisostere
database The Protein Data Bank (PDB) and macromolecular structure
data supporting computer-aided drug design, and the SWISS-MODEL
repository of 3D protein structures and models PDB-REDO in
computational aided drug design (CADD), and mining for bioactive
molecules in databases Unmatched in scope and comprehensively
reviewing small and large open data sources relevant for rational
drug design, Open Access Databases and Datasets for Drug Discovery
is an essential reference for medicinal and pharmaceutical
chemists, and any scientists involved in the drug discovery and
drug development.
Molecular Descriptors for Chemoinformatics As every chemist knows,
there is a direct (if complex) relationship between the molecular
structure of a compound and its chemical behavior. Predicting such
behavior is possible by an abstract representation of its structure
in terms of chemical similarity parameters, socalled
‘descriptors’. These are most useful in predicting the
pharmacological properties of drug candidates, but are also used in
predicting reactivity, toxicity and other important chemical
characteristics. The number-one reference on the topic now contains
a wealth of new data: The entire relevant literature over the past
eight years has been pain-stakingly surveyed, resulting in hundreds
of new descriptors being added to the list, and some 3,000 new
references in the bibliography section. Volume 1 contains an
alphabetical listing of around 3300 terms for the chemoinformatic
analysis of chemical compound properties, while the second volume
contains 6343 references selected from 450 journals with about 7000
authors quoted covering the period from the beginning of molecular
descriptor research until the year 2008. In this second edition,
several sections have been completely rewritten and organized in a
more effective way. The greatly expanded introductory section now
contains several “walk-through” reading lists of selected
keywords to make the data even more accessible for novice users.
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