Chemoinformatics for drug discovery / / edited by Jürgen Bajorath

• Pubblicazione: Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2014]; ©2014
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (415 p.)
• Disciplina: 615.1/9
• Soggetto topico: Cheminformatics; Drug development - Data processing; Pharmacy informatics
• Altri autori: BajorathJürgen
• Note generali: Includes index.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Chemoinformatics for Drug Discovery -- Copyright -- Contents -- Preface -- Contributors -- 1 What Are Our Models Really Telling Us? A Practical Tutorial on Avoiding Common Mistakes When Building Predictive Models -- 1.1 Introduction -- 1.2 Preliminaries -- 1.3 Datasets -- 1.3.1 Exploring Datasets -- 1.4 Building Predictive Models -- 1.5 Evaluating the Performance of Predictive Models -- 1.5.1 Pearson's r -- 1.5.2 Kendall's Tau -- 1.5.3 Root-Mean-Square Deviation (RMSD) -- 1.6 Molecular Descriptors -- 1.7 Building and Testing a Random Forest Model -- 1.8 Experimental Error and Model Performance -- 1.9 Model Applicability -- 1.10 Comparing Predictive Models -- 1.11 Conclusion -- References -- Source Code Listings -- 2 The Challenge of Creativity in Drug Design -- 2.1 Drug Design History: Incrementalism and Serendipity -- 2.2 Physical Reality and Computational Methods -- 2.2.1 Protein Structure-Based Methods -- 2.2.2 Molecular Similarity -- 2.2.3 3D QSAR: Physically Realistic Activity Prediction -- 2.3 Summary -- References -- 3 A Rough Set Theory Approach to the Analysis of Gene Expression Profiles -- 3.1 Introduction -- 3.2 Methodology -- 3.2.1 Basic Theory -- 3.2.2 Measures of Classification Accuracy and Quality -- 3.2.3 An Illustrative Example -- 3.2.4 Essential and Superfluous Information -- 3.2.5 Rule Generation -- 3.3 Drug-Induced Gene Expression and Phospholipidosis in Human Hepatoma HepG2 Cells -- 3.3.1 Dataset -- 3.3.2 Determination of D-Reducts -- 3.3.3 Generation of Preliminary Rules -- 3.3.4 Rule Simplification-Reduction of Attribute Values -- 3.4 Discussion -- 3.5 Summary and Conclusions -- Notes -- References -- 4 Bimodal Partial Least-Squares Approach and Its Application to Chemogenomics Studies for Molecular Design -- 4.1 Introduction -- 4.2 Material and Methods -- 4.2.1 Aminergic GPCR Inhibitory Activity Data. ; 4.2.2 Ligand and Protein Descriptors for LPLS Analysis -- 4.2.3 L-Shaped PLS -- 4.2.4 Atom Colorings Derived from Regression Coefficient Matrix -- 4.3 Results and Discussion -- 4.3.1 LPLS Analysis -- 4.3.2 Atom Colorings and Support by Molecular Modeling -- 4.4 Conclusion -- 4.5 Acknowledgments -- References -- 5 Stability in Molecular Fingerprint Comparison -- 5.1 Introduction -- 5.2 Methods -- 5.2.1 2D Methods -- 5.2.2 Generation of Molecular Isosteres: WABE -- 5.2.3 Tanimoto and Significance -- 5.3 Results -- 5.4 Conclusions and Directions -- References -- 6 Critical Assessment of Virtual Screening for Hit Identification -- 6.1 Introduction -- 6.2 Factors Affecting the Outcome and Evaluation of Virtual Screening Campaigns -- 6.2.1 General Scientific Factors -- 6.2.2 Characteristics of Practical Applications -- 6.3 How to Evaluate Virtual Screening Performance? -- 6.4 Virtual Versus High-Throughput Screening -- 6.4.1 Do We Need Virtual Screening? -- 6.4.2 Underutilized Strengths -- 6.5 Structural Novelty Revisited: Exemplary Cases -- 6.6 Expectations and Selected Applications -- 6.6.1 Inhibitors of Multifunctional Proteins: Cytohesins -- 6.6.2 First-in-Class Inhibitor for Ecto-5′-Nucleotidase -- 6.7 Conclusions: What Is Possible? What Is Not? -- References -- 7 Chemometric Applications of Naïve Bayesian Models in Drug Discovery: Beyond Compound Ranking -- 7.1 Introduction -- 7.1.1 Naïve Bayesian Models -- 7.2 Virtual Screening Using Bayesian Models -- 7.2.1 Reverse Virtual Screening: Target Fishing -- 7.2.2 Comparison to Other Molecular Representations and Machine Learning Techniques -- 7.3 Data Types and Data Quality Requirements -- 7.3.1 Compound Structure -- 7.3.2 Biological Activity -- 7.3.3 Binning of Potency and Guidelines for Multiclass Bayesian Models -- 7.4 Target and Phenotype Comparison in Chemical and Biological Activity Space. ; 7.4.1 Comparison of Compound Classes Using Bayesian Weights -- 7.5 Mining for Enriched Features and Interpreting Them -- 7.5.1 Understanding Chemist's Chemical Preferences -- 7.6 Shortcomings of NBM -- 7.7 Summary -- Acknowledgments -- References -- 8 Chemoinformatics in Lead Optimization -- 8.1 Historical Introduction -- 8.2 Lead Optimization Is a Large, Complex, Multiobjective Process -- 8.3 Chemoinformatics Methods for Multiobjective Optimization -- 8.3.1 Rules of Thumb -- 8.3.2 Filters -- 8.3.3 Desirability Functions -- 8.3.4 Probabilistic Scoring -- 8.3.5 Finding the "Best" Balance of Properties -- 8.4 Case Studies -- 8.4.1 Retrospective Analyses -- 8.4.2 MPO-Guided Hit to Candidate -- 8.5 Conclusion -- References -- 9 Using Chemoinformatics Tools to Analyze Chemical Arrays in Lead Optimization -- 9.1 Introduction -- 9.2 Lead Optimization Projects -- 9.3 Coverage of Chemistry and Property S pace ( DeltaS- DeltaA Plots) -- 9.4 Temporal Analysis of Lead Optimization -- 9.5 Modeling Lead Optimization as a Self-Avoiding Random Walk -- 9.6 Insights from the Data Analysis -- 9.7 Extracting Information on Arrays from the Archive -- 9.7.1 Annotating by Arrays -- 9.7.2 Automatic Chemotype Detector -- 9.7.3 Detecting Seed Compounds -- 9.8 Conclusions -- Acknowledgments -- References -- 10 Exploration of Structure-Activity Relationships (SARs) and Transfer of Key Elements in Lead Optimization -- 10.1 Introduction -- 10.2 Methods for SAR Analysis -- 10.2.1 Similarity Principle -- 10.2.2 Molecular Scaffolds -- 10.2.3 Privileged Substructures -- 10.2.4 Investigating the Outliers: Activity Cliffs -- 10.2.5 Quantification of Activity Cliffs -- 10.2.6 Matched Molecular Pairs -- 10.2.7 Exploration of Activity Cliffs for SAR Analysis -- 10.2.8 Visualization to Support SAR Analysis -- 10.2.9 Solutions in Pharmaceutical Industry -- 10.3 SAR Transfer in Rescaffolding. ; 10.3.1 Concepts of Rescaffolding -- 10.3.2 2D-Based Approaches Beyond 2D-Fingerprints -- 10.3.3 3D-Ligand-Based Approaches -- 10.3.4 3D-Protein-Based Approaches -- 10.4 Addressing Antitarget Activity -- 10.4.1 SAR Transfer in Lead Optimization -- 10.4.2 Identification and Application of Antitarget Activity Hotspots -- 10.4.3 Application Examples to Address hERG and CYP3A4 Inhibition -- 10.4.4 Integration in Lead Optimization Projects -- 10.5 Conclusion -- Acknowledgments -- References -- Chapter 11 Development and Applications of Global ADMET Models: In Silico Prediction of Human Microsomal Lability -- 11.1 Introduction -- 11.1.1 Structure-Based ADMET Models -- 11.1.2 Ligand-Based ADMET Models -- 11.2 Case Study on Metabolic Lability -- 11.2.1 Model Building -- 11.2.2 Dataset -- 11.2.3 Results -- 11.2.4 Application of a Global Model for Metabolic Lability in the Optimization of DGAT1 Inhibitors -- 11.3 Conclusion -- References -- Chapter 12 Chemoinformatics and Beyond: Moving from Simple Models to Complex Relationships in Pharmaceutical Computational Toxicology -- 12.1 Introduction -- 12.2 Data-Driven Modeling -- 12.2.1 Linking Chemical Structure to In Vitro Results -- 12.2.2 Chemical Structure and Using All Available Data -- 12.2.3 Combination of Evidence -- 12.2.4 Focusing on Biological Data -- 12.3 Delivering Impact: Bringing It to the Customer -- 12.3.1 Technical Solution -- 12.3.2 Facilitate Usage -- 12.4 Summary and Outlook -- Acknowledgments -- References -- Chapter 13 Applications of Cheminformatics in Pharmaceutical Research: Experiences at Boehringer Ingelheim in Germany -- 13.1 Introduction -- 13.2 Infrastructure and Systems -- 13.2.1 General Overview -- 13.2.2 Cheminformatics Database (CIDB) -- 13.2.3 Workflow Systems -- 13.2.4 BI Chemical Property Structure Planning System (BICEPS) -- 13.2.5 Project Data Marts. ; 13.2.6 Database of Virtual Combinatorial Libraries (BICLAIM-DB) -- 13.3 Methods and Applications -- 13.3.1 BIMESH : The HTS Data Analysis -- 13.3.2 BioProfile -- 13.3.3 SAR Analysis -- 13.3.4 Searches in BICLAIM -Space -- 13.3.5 Automatic Annotation of Controlled Substances -- 13.4 Discussion -- References -- Chapter 14 Lessons Learned from 30 Years of Developing Successful Integrated Cheminformatic Systems -- 14.1 Introduction -- 14.2 History -- 14.2.1 Cousin: 1981-2001 -- 14.2.2 ChemLink : 2001-2003 -- 14.2.3 RGate : 2003+ -- 14.2.4 Beacon Projects (~2000-2004) -- 14.2.5 Mobius: 2005 Till Present -- 14.3 Keys to the Success of Mobius: A Technical Perspective -- 14.3.1 Data Sources -- 14.3.2 Metaview -- 14.3.3 Query Engine -- 14.3.4 Ad Hoc Query Interface -- 14.3.5 Software Components -- 14.4 Lessons Learned: The Bottom Line -- 14.4.1 Quality Software -- 14.4.2 Data, Data, Data -- 14.4.3 Commitment to the User -- 14.4.4 Continuity -- 14.4.5 Importance of Developers That Understand the Science -- 14.4.6 Speed Kills -- 14.4.7 Bottom-Up Beats Top-Down -- 14.4.8 Use Off-the-Shelf Whenever Possible -- 14.4.9 Rollout Systems Using the Apostle Approach -- 14.4.10 Training -- 14.4.11 Support/Maintenance -- 14.4.12 Succession Planning -- 14.5 Build Versus Buy Versus Open Source -- 14.6 Conclusions and Summary -- References -- Chapter 15 Molecular Similarity Analysis -- 15.1 Introduction -- 15.2 A Brief History of Molecular Similarity Analysis -- 15.3 Cognitive Aspects of Similarity -- 15.4 Molecular Similarity Measures -- 15.4.1 Mathematical Description of Molecular Similarity -- 15.4.2 Representing Molecular and Chemical Information -- 15.4.3 Weighted Representations -- 15.4.4 Molecular Similarity Functions or Coefficients -- 15.5 Some Issues in Molecular Similarity Analysis -- 15.5.1 Asymmetric Similarity -- 15.5.2 2D and 3D Similarity Methods. ; 15.5.3 Data Fusion and Consensus Methods.
• Sommario/riassunto: Chemoinformatics strategies to improve drug discovery results With 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. Chemoinformatics for Drug Discovery 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: * What are our models really telling us? A practical tutorial on avoiding common mistakes when building predictive models * Exploration of structure-activity relationships and transfer of key elements in lead optimization * Collaborations between academia and pharma * Applications of chemoinformatics in pharmaceutical research-experiences at large international pharmaceutical companies * Lessons learned from 30 years of developing successful integrated chemoinformatic systems Throughout 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. Integrating chemistry, computer science, and drug discovery, Chemoinformatics for Drug Discovery encapsulates the field as it stands today and opens the door to further advances.
• Titolo autorizzato: Chemoinformatics for drug discovery
• ISBN: 9781118743096; 1118743091; 9781118742785; 1118742788; 9781118743058; 1118743059
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910829021703321
• Lo trovi qui: Univ. Federico II