{"product_id":"chemoinformatics-for-drug-discovery-isbn-9781118139103","title":"Chemoinformatics for Drug Discovery","description":"\u003cp\u003e\u003cb\u003eChemoinformatics strategies to improve drug discovery results\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWith contributions from leading researchers in academia and the pharmaceutical industry as well as experts from the software industry, this book explains how chemoinformatics enhances drug discovery and pharmaceutical research efforts, describing what works and what doesn't. Strong emphasis is put on tested and proven practical applications, with plenty of case studies detailing the development and implementation of chemoinformatics methods to support successful drug discovery efforts. Many of these case studies depict groundbreaking collaborations between academia and the pharmaceutical industry.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eChemoinformatics for Drug Discovery\u003c\/i\u003e is logically organized, offering readers a solid base in methods and models and advancing to drug discovery applications and the design of chemoinformatics infrastructures. The book features 15 chapters, including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eWhat are our models really telling us? A practical tutorial on avoiding common mistakes when building predictive models\u003c\/li\u003e \u003cli\u003eExploration of structure-activity relationships and transfer of key elements in lead optimization\u003c\/li\u003e \u003cli\u003eCollaborations between academia and pharma\u003c\/li\u003e \u003cli\u003eApplications of chemoinformatics in pharmaceutical research—experiences at large international pharmaceutical companies\u003c\/li\u003e \u003cli\u003eLessons learned from 30 years of developing successful integrated chemoinformatic systems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThroughout the book, the authors present chemoinformatics strategies and methods that have been proven to work in pharmaceutical research, offering insights culled from their own investigations. Each chapter is extensively referenced with citations to original research reports and reviews.\u003c\/p\u003e \u003cp\u003eIntegrating chemistry, computer science, and drug discovery, \u003ci\u003eChemoinformatics for Drug Discovery\u003c\/i\u003e encapsulates the field as it stands today and opens the door to further advances.\u003c\/p\u003e \u003cp\u003ePreface vii\u003c\/p\u003e \u003cp\u003eContributors xiii\u003c\/p\u003e \u003cp\u003e1 What Are Our Models Really Telling Us? A Practical Tutorial on Avoiding Common Mistakes when Building Predictive Models 1\u003cbr\u003e \u003ci\u003eW. Patrick Walters\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 The Challenge of Creativity in Drug Design 33\u003cbr\u003e \u003ci\u003eAjay N. Jain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 A Rough Set Theory Approach to the Analysis of Gene Expression Profiles 51\u003cbr\u003e \u003ci\u003eJoachim Petit, Nathalie Meurice, José Luis Medina-Franco, and Gerald M. Maggiora\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 Bimodal Partial Least-Squares Approach and Its Application to Chemogenomics Studies for Molecular Design 85\u003cbr\u003e \u003ci\u003eKiyoshi Hasegawa and Kimito Funatsu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 Stability in Molecular Fingerprint Comparison 97\u003cbr\u003e \u003ci\u003eAnthony Nicholls and Brian Kelley\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 Critical Assessment of Virtual Screening for Hit Identification 113\u003cbr\u003e \u003ci\u003eDagmar Stumpfe and Jürgen Bajorath\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 Chemometric Applications of Naïve Bayesian Models in Drug Discovery: Beyond Compound Ranking 131\u003cbr\u003e \u003ci\u003eEugen Lounkine, Peter S. Kutchukian, and Meir Glick\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Chemoinformatics in Lead Optimization 149\u003cbr\u003e \u003ci\u003eDarren V. S. Green and Matthew Segall\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 Using Chemoinformatics Tools to Analyze Chemical Arrays in Lead Optimization 179\u003cbr\u003e \u003ci\u003eGeorge Papadatos, Valerie J. Gillet, Christopher N. Luscombe, Iain M. McLay, Stephen D. Pickett, and Peter Willett\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 Exploration of Structure–Activity Relationships (SARs) and Transfer of Key Elements in Lead Optimization 205\u003cbr\u003e \u003ci\u003eHans Matter, Stefan Güssregen, Friedemann Schmidt, Gerhard Hessler, Thorsten Naumann, and Karl-Heinz Baringhaus\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 Development and Applications of Global ADMET Models: In Silico Prediction of Human Microsomal Lability 245\u003cbr\u003e \u003ci\u003eKarl-Heinz Baringhaus, Gerhard Hessler, Hans Matter, and Friedemann Schmidt\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 Chemoinformatics and Beyond: Moving from Simple Models to Complex Relationships in Pharmaceutical Computational Toxicology 267\u003cbr\u003e \u003ci\u003eCatrin Hasselgren, Daniel Muthas, Ernst Ahlberg, Samuel Andersson, Lars Carlsson, Tobias Noeske, Jonna Stålring, and Scott Boyer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13 Applications of Cheminformatics in Pharmaceutical Research: Experiences at Boehringer Ingelheim in Germany 291\u003cbr\u003e \u003ci\u003eBernd Beck, Michael Bieler, Peter Haebel, Andreas Teckentrup, Alexander Weber, and Nils Weskamp\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 Lessons Learned from 30 Years of Developing Successful Integrated Cheminformatic Systems 321\u003cbr\u003e \u003ci\u003eMichael S. Lajiness and Thomas R. Hagadone\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 Molecular Similarity Analysis 343\u003cbr\u003e \u003ci\u003eJosé L. Medina-Franco and Gerald M. Maggiora\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIndex 401\u003c\/p\u003e  \u003cp\u003e“Overall the book is well written, logically following the larger storyline and most of the time offering high quality reviews . . . Personally, I enjoyed reading this book and would recommend it as a general introduction to the subject aimed especially for post-graduate students and non-specialists working in the area of drug design, but also for all of those who just want to update their knowledge.”  (\u003ci\u003eChemMedChem\u003c\/i\u003e, 1 June 2014)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eJÜRGEN BAJORATH, PhD,\u003c\/b\u003e is Chair of Life Science Informatics at the University of Bonn and also an Affiliate Professor in the Department of Biological Structure at the University of Washington. In addition, he has more than 10 years' experience in drug disovery. His research focuses on the development of computational methods for medicinal chemistry and chemical biology. Dr. Bajorath holds 26 patents, has authored more than 400 scientific articles, and edited four books.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eChemoinformatics strategies to improve drug discovery results\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWith contributions from leading researchers in academia and the pharmaceutical industry as well as experts from the software industry, this book explains how chemoinformatics enhances drug discovery and pharmaceutical research efforts, describing what works and what doesn't. Strong emphasis is put on tested and proven practical applications, with plenty of case studies detailing the development and implementation of chemoinformatics methods to support successful drug discovery efforts. Many of these case studies depict groundbreaking collaborations between academia and the pharmaceutical industry.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eChemoinformatics for Drug Discovery\u003c\/i\u003e is logically organized, offering readers a solid base in methods and models and advancing to drug discovery applications and the design of chemoinformatics infrastructures. The book features 15 chapters, including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eWhat are our models really telling us? A practical tutorial on avoiding common mistakes when building predictive models\u003c\/li\u003e \u003cli\u003eExploration of structure-activity relationships and transfer of key elements in lead optimization\u003c\/li\u003e \u003cli\u003eCollaborations between academia and pharma\u003c\/li\u003e \u003cli\u003eApplications of chemoinformatics in pharmaceutical research—experiences at large international pharmaceutical companies\u003c\/li\u003e \u003cli\u003eLessons learned from 30 years of developing successful integrated chemoinformatic systems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThroughout the book, the authors present chemoinformatics strategies and methods that have been proven to work in pharmaceutical research, offering insights culled from their own investigations. Each chapter is extensively referenced with citations to original research reports and reviews.\u003c\/p\u003e \u003cp\u003eIntegrating chemistry, computer science, and drug discovery, \u003ci\u003eChemoinformatics for Drug Discovery\u003c\/i\u003e encapsulates the field as it stands today and opens the door to further advances.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988910391525,"sku":"NP9781118139103","price":144.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118139103.jpg?v=1761782016","url":"https:\/\/k12savings.com\/products\/chemoinformatics-for-drug-discovery-isbn-9781118139103","provider":"K12savings","version":"1.0","type":"link"}