Puzyn / Leszczynski / Cronin Recent Advances in QSAR Studies

Methods and Applications
1. Auflage 2010
ISBN: 978-1-4020-9783-6
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 8, 414 Seiten

Reihe: Challenges and Advances in Computational Chemistry and Physics

This book presents an interdisciplinary overview on the most recent advances in QSAR studies. The first part consists of a comprehensive review of QSAR methodology. The second part highlights the interdisciplinary aspects and new areas of QSAR modelling.

Autoren/Hrsg.

Leszczynski, Jerzy

Puzyn, Tomasz

Cronin, Mark T.

Inhaltsverzeichnis

1;Preface;6
2;Contents;7
3;Part I Theory of QSAR;15
3.1;1 Quantitative Structure--Activity Relationships (QSARs) -- Applications and Methodology;16
3.1.1;1.1 Introduction;16
3.1.2;1.2 Purpose of QSAR;17
3.1.3;1.3 Applications of QSAR;17
3.1.4;1.4 Methods;18
3.1.5;1.5 The Cornerstones of Successful Predictive Models;20
3.1.6;1.6 A Validated (Q)SAR or a Valid Prediction?;22
3.1.7;1.7 Using in Silico Techniques;22
3.1.8;1.8 New Areas for in Silico Models;24
3.1.9;1.9 Conclusions;24
3.1.10;References;24
3.2;2 The Use of Quantum Mechanics Derived Descriptors in Computational Toxicology;25
3.2.1;2.1 Introduction;25
3.2.2;2.2 The Schrdinger Equation;27
3.2.3;2.3 HartreeFock Theory;29
3.2.4;2.4 Semi-Empirical Methods: AM1 and RM1;30
3.2.5;2.5 AB Initio: Density Functional Theory;31
3.2.6;2.6 QSAR for Non-Reactive Mechanisms of Acute (Aquatic) Toxicity;31
3.2.7;2.7 QSAR s for Reactive Toxicity Mechanisms;33
3.2.7.1;2.7.1.Aquatic Toxicity and Skin Sensitisation;33
3.2.7.2;2.7.2.QSARs for Mutagenicity;36
3.2.8;2.8 Future Directions and Outlook;37
3.2.9;2.9 Conclusions;38
3.2.10;Acknowledgement;38
3.2.11;References;38
3.3;3 Molecular Descriptors;41
3.3.1;3.1 Introduction;41
3.3.1.1;3.1.1.Definitions;41
3.3.1.2;3.1.2.History;43
3.3.1.3;3.1.3.Theoretical vs. Experimental Descriptors;45
3.3.2;3.2 Molecular Representation;47
3.3.3;3.3 Topological Indexes;50
3.3.3.1;3.3.1.Molecular Graphs;50
3.3.3.2;3.3.2.Definition and Calculation of Topological Indexes (TIs);51
3.3.3.3;3.3.3.Graph-Theoretical Matrixes;54
3.3.3.4;3.3.4.Connectivity Indexes;60
3.3.3.5;3.3.5.Characteristic Polynomial;62
3.3.3.6;3.3.6.Spectral Indexes;65
3.3.4;3.4 Autocorrelation Descriptors;67
3.3.4.1;3.4.1.Introduction;67
3.3.4.2;3.4.2.Moreau--Broto Autocorrelation Descriptors;69
3.3.4.3;3.4.3.Moran and Geary Coefficients;71
3.3.4.4;3.4.4.Auto-cross-covariance Transforms;72
3.3.4.5;3.4.5.Autocorrelation of Molecular Surface Properties;75
3.3.4.6;3.4.6.Atom Pairs;75
3.3.4.7;3.4.7.Estrada Generalized Topological Index;78
3.3.5;3.5 Geometrical Descriptors;80
3.3.5.1;3.5.1.Introduction;80
3.3.5.2;3.5.2.Indexes from the Geometry Matrix;81
3.3.5.3;3.5.3.WHIM Descriptors;89
3.3.5.4;3.5.4.GETAWAY Descriptors;93
3.3.5.5;3.5.5.Molecular Transforms;102
3.3.6;3.6 Conclusions;105
3.3.7;References;106
3.4;4 3D-QSAR -- Applications, Recent Advances, and Limitations;115
3.4.1;4.1 Introduction;115
3.4.2;4.2 Why is 3D-QSAR so Attractive?;116
3.4.3;4.3 Ligand Alignment;117
3.4.4;4.4 CMFA and Related Methods;119
3.4.4.1;4.4.1.CoMFA;119
3.4.4.2;4.4.2.CoMSIA;120
3.4.4.3;4.4.3.GRID/GOLPE;121
3.4.4.4;4.4.4.4D-QSAR and 5D-QSAR;121
3.4.4.5;4.4.5.AFMoC;121
3.4.5;4.5 Reliability of 3D-QSAR Models;122
3.4.6;4.6 Receptor-Based 3D-QSAR;124
3.4.7;4.7 Conclusion;131
3.4.8;References;131
3.5;5 Virtual Screening and Molecular Design Based on Hierarchical QSAR Technology;138
3.5.1;5.1 Introduction;139
3.5.2;5.2 Multi-Hierarchical Strategy of QSAR Investigation;141
3.5.2.1;5.2.1.HiT QSAR Concept;141
3.5.2.2;5.2.2.Hierarchy of Molecular Models;143
3.5.2.2.1;5.3.2.1 Simplex Representation of Molecular Structure (SiRMS);143
3.5.2.2.2;5.3.2.2 Lattice Model;148
3.5.2.2.3;5.3.2.3 Whole-Molecule Descriptors and Fourier Transform of Local Parameters;150
3.5.2.3;5.2.3.Hierarchy of Statistical Methods;151
3.5.2.3.1;5.3.3.1 Classification Trees;151
3.5.2.3.2;5.3.3.2 Trend-Vector;152
3.5.2.3.3;5.3.3.3 Multiple Linear Regression;153
3.5.2.3.4;5.3.3.4 Partial Least Squares or Projection to Latent Structures (PLS);154
3.5.2.4;5.2.4.Data Cleaning and Mining;154
3.5.2.4.1;5.3.4.1 Automatic Variable Selection (AVS) Strategy in PLS;155
3.5.2.4.2;5.3.4.2 Genetic Algorithms;155
3.5.2.4.3;5.3.4.3 Enumerative Techniques;155
3.5.2.5;5.2.5.Validation of QSAR Models;156
3.5.2.6;5.2.6.Hierarchy of Aims of QSAR Investigation;158
3.5.2.6.1;5.3.6.1 Virtual Screening (Including Consensus Modeling and DA);159
3.5.2.6.2;5.3.6.2 Inverse Task Solution and Interpretation of QSAR Models;162
3.5.2.6.3;5.3.6.3 Molecular Design;163
3.5.2.7;5.2.7.HiT QSAR Software;164
3.5.3;5.3 Comparative Analysis of HT QSAR Efficiency;165
3.5.3.1;5.3.1.Angiotensin Converting Enzyme (ACE) Inhibitors;166
3.5.3.2;5.3.2.Acetylcholinesterase (AChE) Inhibitors;167
3.5.4;5.4 HT QSAR Applications;169
3.5.4.1;5.4.1.Antiviral Activity;169
3.5.4.1.1;5.5.1.1 Antiherpetic Activity of N , N 0-(bis-5-nitropyrimidyl)Dispirotripiperazine Derivatives The authors express sincere gratitude to Dr. M. Schmidtke, Prof. P. Wutzler, Dr. V. Makarov, Dr. O. Riabova, Mr. N. Kovdienko and Mr. A. Hromov for fruitful cooperation that made the development of this task possible. (2D);169
3.5.4.1.2;5.5.1.2 Antiherpetic Activity of Macrocyclic Pyridinophanes Anti-influenza and antiherpetic investigations described below were carried out as a result of fruitful cooperation with Dr. V.P. Lozitsky, Dr. R.N. Lozytska, Dr. A.S. Fedtchouk, Dr. T.L. Gridina, Dr. S. Basok, Dr. D. Chikhichin, Mr. V. Chelombitko and Dr. J.-J. Vanden Eynde. The authors express sincere gratitude for all mentioned above colleagues. ;170
3.5.4.1.3;5.5.1.3 [(Biphenyloxy)propyl]isoxazole Derivatives 0 Human Rhinovirus 2 Replication Inhibitors The authors express sincere gratitude to Dr. M. Schmidtke, Prof. P. Wutzler, Dr. V. Makarov, Dr. O. Riabova and Ms. Volineckaya for fruitful cooperation that made possible the development of this task. (2D);172
3.5.4.1.4;5.5.1.4 Anti-influenza Activity of Macrocyclic Pyridinophanes 4 (2D--4D);173
3.5.4.2;5.4.2.Anticancer Activity of MacroCyclic Schiff Bases 8 The authors express sincere gratitude to Dr. V.P. Lozitsky, Dr. R.N. Lozytska and Dr. A.S. Fedtchouk for fruitful cooperation during the development of this task. (2D and 4D);175
3.5.4.3;5.4.3.Acute Toxicity of Nitroaromatics;176
3.5.4.3.1;5.5.3.1 Toxicity to Rats The authors express sincere gratitude to Prof. J. Leszczynski, Dr. L. Gorb and Dr. M. Quasim for fruitful cooperation during the development of this task. (1D--2D);176
3.5.4.3.2;5.5.3.2 Toxicity to Tetrahymena Pyriformis The authors express sincere gratitude to Prof. J. Leszczynski, Dr. L. Gorb, Dr. M. Quasim and Prof. A. Tropsha for fruitful cooperation during the development of this task. (2D);177
3.5.4.4;5.4.4.AChE Inhibition The authors express sincere gratitude to Prof. J. Leszczynski, Dr. L. Gorb and Dr. J. Wang for fruitful cooperation during the development of this task. (2.5D, Double 2.5D, and 3D);178
3.5.4.5;5.4.5.5-HT 1A Affinity (1D04D) The authors express sincere gratitude to Academician S.A. Andronati and Dr. S.Yu. Makan for fruitful cooperation during the development of this task. ;179
3.5.4.6;5.4.6.Pharmacokinetic Properties of Substituted Benzodiazepines (2D);180
3.5.4.7;5.4.7.Catalytic Activity of Crown Ethers The authors express sincere gratitude to Prof. G.L. Kamalov, Dr. S.A. Kotlyar and Dr. G.N. Chuprin for fruitful cooperation during the development of this task. (3D);181
3.5.4.8;5.4.8.Aqueous Solubility The authors express sincere gratitude to Prof. J. Leszczynski, Dr. L. Gorb and Dr. M. Quasim for fruitful cooperation during the development of this task. (2D);182
3.5.5;5.5 Conclusions;183
3.5.6;References;183
3.6;6 Robust Methods in QSAR;188
3.6.1;6.1 Introduction;188
3.6.2;6.2 Outliers and their genesis in the QSAR Studies;190
3.6.3;6.3 Major Concepts of Robustness;192
3.6.3.1;6.3.1.The Breakdown Point of an Estimator;192
3.6.3.2;6.3.2.Influence Function of an Estimator;192
3.6.3.3;6.3.3.Efficiency of an Estimator;192
3.6.3.4;6.3.4.Equivariance Properties of an Estimator;193
3.6.4;6.4 Robust Estimators;194
3.6.4.1;6.4.1.Robust Estimators of Data Location and Scatter;194
3.6.4.2;6.4.2.Robust Estimators for Multivariate Data Location and Covariance;196
3.6.5;6.5 Exploring the Space of Molecular Descriptors;198
3.6.5.1;6.5.1.Classic Principal Component Analysis;198
3.6.5.2;6.5.2.Robust Variants of Principal Component Analysis;199
3.6.5.2.1;6.6.2.1 Spherical and Elliptical PCA;200
3.6.5.2.2;6.6.2.2 Projection Pursuit with the Qn Scale;202
3.6.5.2.3;6.6.2.3 ROBPCA -- A Robust Variant of PCA;202
3.6.6;6.6 Construction of Multivariate QSAR Models;203
3.6.6.1;6.6.1.Classic Partial Least Squares Regression;203
3.6.6.2;6.6.2.Robust Variants of the Partial Least Squares Regression;204
3.6.6.2.1;6.7.2.1 Partial Robust M-Regression;204
3.6.6.2.2;6.7.2.2 Robust Version of PLS via the Spatial Sign Preprocessing;206
3.6.6.2.3;6.7.2.3 RSIMPLS and RSIMCD -- Robust Variants of SIMPLS;207
3.6.6.3;6.6.3.Outlier Diagnostics Using Robust Approaches;207
3.6.7;6.7 Examples of Applications;209
3.6.7.1;6.7.1.Description of the Data Sets Used to Illustrate Performance of Robust Methods;209
3.6.7.2;6.7.2.Identification of Outlying Molecules Using the Robust PCA Model;210
3.6.7.3;6.7.3.Construction of the Robust QSAR Model with the PRM Approach;213
3.6.8;6.8 Concluding Remarks and Further Readings;216
3.6.9;References;216
3.7;7 Chemical Category Formation and Read-Across for the Prediction of Toxicity;220
3.7.1;7.1 Introduction;220
3.7.2;7.2 Benefits of the Category Formation;221
3.7.3;7.3 Chemical Similarity;221
3.7.4;7.4 General Approach to Chemical Category Formation;222
3.7.5;7.5 Examples of Category Formation and Read-Across;223
3.7.5.1;7.5.1.Chemical Class-Based Categories;224
3.7.5.2;7.5.2.Mechanism-Based Categories;224
3.7.5.3;7.5.3.Chemoinformatics-Based Categories;227
3.7.6;7.6 Conclusions;228
3.7.7;Acknowledgement;228
3.7.8;References ;228
4;Part II Practical Application;231
4.1;8 QSAR in Chromatography: Quantitative Structure--Retention Relationships (QSRRs);232
4.1.1;8.1 Introduction;232
4.1.1.1;8.1.1.Methodology of QSRR Studies;232
4.1.1.2;8.1.2.Intermolecular Interactions and Structural Descriptors of Analytes;235
4.1.2;8.2 Chromatographic Retention Predictions;239
4.1.2.1;8.2.1.Retention Predictions in View of Optimization of HPLC Separations;240
4.1.2.2;8.2.2.Retention Predictions in Proteomics Research;248
4.1.3;8.3 Characterization of Stationary Phases;249
4.1.4;8.4 Assessment of Lipophilicity by QSRR;252
4.1.5;8.5 QSRR in Affinity Chromatography;257
4.1.6;8.6 Conclusions;259
4.1.7;Acknowledgement;260
4.1.8;References;260
4.2;9 The Use of QSAR and Computational Methods in Drug Design;269
4.2.1;9.1 Introduction;269
4.2.2;9.2 From New Chemical Entities (Nce) To Drug Candidates: Preclinical Phases;270
4.2.2.1;9.2.1.Stage 1: Hit Finding;270
4.2.2.2;9.2.2.Stage 2: Lead Finding;271
4.2.2.3;9.2.3.Stage 3: Lead Optimization;272
4.2.3;9.3 Failure in Drug Candidates Development;272
4.2.4;9.4 Classic Qsar in Drug Design;273
4.2.4.1;9.4.1.Hansch Analysis;273
4.2.4.2;9.4.2.Non-parametric Methods: Free-Wilson and Fujita-Ban;274
4.2.4.3;9.4.3.Linear Solvation Free-Energy Relationships (LSERs);274
4.2.5;9.5 QSAR Methods in Modern Drug Design;276
4.2.5.1;9.5.1.Tools for QSAR;277
4.2.5.1.1;9.6.1.1 Data and Databases;277
4.2.5.1.2;9.6.1.2 Novel Molecular Descriptors;278
4.2.5.1.3;9.6.1.3 3D-QSAR;279
4.2.5.1.4;9.6.1.4 Applicability Domain in QSARs;281
4.2.6;9.6 QSAR in Modern Drug Design: Examples;282
4.2.6.1;9.6.1.Example 1: Application of QSAR to Predict hERG Inhibition;282
4.2.6.2;9.6.2.Example 2: Application of QSAR to Predict Blood--Brain Barrier Permeation;283
4.2.6.3;9.6.3.Example 3: Application of QSAR to Predict COX-2 Inhibition;284
4.2.7;9.7 Conclusion and Perspectives;286
4.2.8;References;286
4.3;10 In Silico Approaches for Predicting ADME Properties;291
4.3.1;10.1 Introduction;291
4.3.1.1;10.1.1.Overview of Key ADME Properties;292
4.3.1.2;10.1.2.Data for Generation of in Silico Models;297
4.3.2;10.2 Models for the Prediction of ADME Properties;299
4.3.3;10.3 Software Developments;302
4.3.4;10.4 Selecting the Most Appropriate Modeling Approach;305
4.3.5;10.5 Future Direction;306
4.3.6;10.6 Conclusion;309
4.3.7;Acknowledgement;309
4.3.8;References;309
4.4;11 Prediction of Harmful Human Health Effects of Chemicals from Structure;313
4.4.1;11.1 Introduction;313
4.4.1.1;11.1.1.Prediction of Harmful Effects to Man?;314
4.4.1.2;11.1.2.Relevant Toxicity Endpoints Where QSAR Can Make a Significant Contribution;315
4.4.2;11.2 In Silico Tools for Toxicity Prediction;316
4.4.2.1;11.2.1.Databases;316
4.4.2.2;11.2.2.QSARs;319
4.4.2.2.1;11.3.2.1 Skin Sensitization Data for Modeling;319
4.4.2.2.2;11.3.2.2 SAR (Qualitative) Models for Skin Sensitization;319
4.4.2.2.3;11.3.2.3 QSAR Models for Skin Sensitization;320
4.4.2.2.4;11.3.2.4 General Comments of the Use of QSAR Models for Predicting Human Health Effects;322
4.4.2.3;11.2.3.Expert Systems;323
4.4.2.4;11.2.4.Grouping Approaches;326
4.4.3;11.3 The Future of in Silico Toxicity Prediction;328
4.4.3.1;11.3.1.Consensus (Q)SAR Models;328
4.4.3.2;11.3.2.Integrated Testing Strategies (ITS);329
4.4.4;11.4 Conclusions;330
4.4.5;Acknowledgement;330
4.4.6;References;330
4.5;12 Chemometric Methods and Theoretical Molecular Descriptors in Predictive QSAR Modeling of the Environmental Behavior of Organic Pollutants;334
4.5.1;12.1 Introduction;334
4.5.2;12.2 A Defined Endpoint (OECD Principle 1);336
4.5.3;12.3 An Unambiguous Algorithm (OECD Principle 2);336
4.5.3.1;12.3.1.Chemometric Methods;337
4.5.3.1.1;12.4.1.1 Regression Models;337
4.5.3.1.2;12.4.1.2 Classification Models;337
4.5.3.2;12.3.2.Theoretical Molecular Descriptors;339
4.5.3.3;12.3.3.Variable Selection and Reduction. The Genetic Algorithm Strategy for Variable Selection;340
4.5.4;12.4 Applicability Domain (OECD Principle 3);341
4.5.5;12.5 Model Validation for Predictivity (OECD Principle 4);343
4.5.5.1;12.5.1.Splitting of the Data Set for the Construction of an External Prediction Set;344
4.5.5.2;12.5.2.Internal and External Validation;345
4.5.5.3;12.5.3.Validation of Classification Models;346
4.5.6;12.6 Molecular Descriptor Interpretation, If Possible (OECD Principle 5);347
4.5.7;12.7 Environmental Single Endpoints;347
4.5.7.1;12.7.1.Physico-chemical Properties;347
4.5.7.1.1;12.8.1.1 Soil Sorption of Pesticides;348
4.5.7.2;12.7.2.Tropospheric Reactivity of Volatile Organic Compounds with Oxidants;350
4.5.7.3;12.7.3.Biological Endpoints;352
4.5.7.3.1;12.8.3.1 Bioconcentration Factor;352
4.5.7.3.2;12.8.3.2 Toxicity;353
4.5.8;12.8 Modeling More than a Single Endpoint;357
4.5.8.1;12.8.1.PC Scores as New Endpoints: Ranking Indexes;357
4.5.8.2;12.8.2.Multivariate Explorative Methods;357
4.5.8.2.1;12.9.2.1 Principal Component Analysis;358
4.5.8.2.2;12.9.2.2 QSAR Modeling of Ranking Indexes;358
4.5.9;12.9 Conclusions;366
4.5.10;Acknowledgement;366
4.5.11;References;366
4.6;13 The Role of QSAR Methodology in the Regulatory Assessment of Chemicals;374
4.6.1;13.1 Introduction;374
4.6.2;13.2 Basic Concepts;375
4.6.3;13.3 The Regulatory Use of (Q)SAR Methods;376
4.6.4;13.4 The Validity, Applicability, and Adequacy of (Q)SARs;377
4.6.4.1;13.4.1.Demonstrating Validity;378
4.6.4.2;13.4.2.Demonstrating Applicability;380
4.6.4.3;13.4.3.Demonstrating Adequacy;381
4.6.5;13.5 The Integrated Use of (Q)SARs;383
4.6.5.1;13.5.1.Stepwise Approach for Using (Q)SAR Methods;383
4.6.5.2;13.5.2.Use of (Q)SARs in Chemical Categories;384
4.6.5.3;13.5.3.Use of (Q)SARs in Integrated Testing Strategies;385
4.6.6;13.6 Conclusions;386
4.6.7;References;386
4.7;14 Nanomaterials -- the Next Great Challenge for QSAR Modelers;390
4.7.1;14.1 Increasing Role of Nanomaterials;390
4.7.2;14.2 Their Incredible Physical and Chemical Properties;391
4.7.3;14.3 Nanomaterials can be Toxic;392
4.7.3.1;14.3.1.Specific Properties Cause Specific Toxicity;393
4.7.3.2;14.3.2.Oxidative Stress;394
4.7.3.3;14.3.3.Cytotoxicity and Genotoxicity;394
4.7.3.4;14.3.4.Neurotoxicity;394
4.7.3.5;14.3.5.Immunotoxicity;395
4.7.3.6;14.3.6.Ecotoxicity;395
4.7.4;14.4 NANO-QSAR Advances and Challenges;396
4.7.4.1;14.4.1.Description of Structure;397
4.7.4.2;14.4.2.Nanostructure -- Electronic Properties Relationships;402
4.7.4.3;14.4.3.Nano-QSAR Models;403
4.7.5;14.5 Summary;409
4.7.6;Acknowledgement;410
4.7.7;References;410
5;Appendix A;417
6;Index;420