Open Access Databases and Datasets for Drug Discovery (Methods and Principles in Medicinal Chemistry) - Hardcover

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Antoine Daina studied Pharmacy at the University of Lausanne (Switzerland) and got a Ph.D. in Pharmaceutical Sciences from the University of Geneva. After industrial practice as computational chemist for agrochemical research and academic experience as lecturer and researcher in drug discovery, he joined the SIB Swiss Institute of Bioinformatics in 2012. He is now Senior Scientist in the Molecular Modeling Group in charge of methodological developments in the SwissDrugDesign program, of supporting drug discovery projects and of teaching computer-aided drug design. Author of 22 peer-reviewed research articles or reviews and co-inventor on 5 patents.
 
Michael Przewosny studied chemistry at RWTH Aachen (Germany) and obtained a PhD in the field of peptides and protein chemistry at the DWI Aachen. He has over 20 years of experience in pharmaceutical research and drug discovery, having held several positions as laboratory manager in medicinal chemistry and process development. He is listed as co-inventor on 27 patents.
 
Vincent Zoete studied Chemistry at the Ecole Nationale Supérieure de Chimie de Lille and at Lille University (France). He studied Molecular Modeling in the Karplus Lab is Strasbourg and joined the Swiss Institute of Bioinformatics (SIB) in 2004. He was Associate Group leader of the SIB Molecular Modeling Group until 2017, then Group Leader from 2017 until now. He is also an Assistant Professor in Molecular Modelling at the University of Lausanne since 2017. He is the coordinator/developer of SwissDock.ch, SwissParam.ch, SwissBioisostere.ch, SwissTargetPrediction.ch, SwissSimilarity.ch, SwissADME.ch and the author of 122 peer-reviewed research articles and reviews, and 5 patents.

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Timely 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. 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:

• An extensive 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 using Pharos/TCRD for discovering druggable targets

Unmatched in scope and thoroughly 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.