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.