| Edizione | [1st ed.] |
| Pubbl/distr/stampa |
Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2014]
|
| Descrizione fisica |
1 online resource (415 p.)
|
| Disciplina |
615.1/9
|
| Altri autori (Persone) |
BajorathJürgen
|
| Soggetto topico |
Cheminformatics
Drug development - Data processing
Pharmacy informatics
|
| ISBN |
1-118-74309-1
1-118-74278-8
1-118-74305-9
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| 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.
|
| Record Nr. | UNINA-9910829021703321 |
Info
Chemoinformatics for drug discovery / / edited by Jürgen Bajorath
