Roy / Kar / Das Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment

1. Auflage 2015
ISBN: 978-0-12-801633-6
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 484 Seiten

Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment describes the historical evolution of quantitative structure-activity relationship (QSAR) approaches and their fundamental principles. This book includes clear, introductory coverage of the statistical methods applied in QSAR and new QSAR techniques, such as HQSAR and G-QSAR. Containing real-world examples that illustrate important methodologies, this book identifies QSAR as a valuable tool for many different applications, including drug discovery, predictive toxicology and risk assessment. Written in a straightforward and engaging manner, this is the ideal resource for all those looking for general and practical knowledge of QSAR methods. - Includes numerous practical examples related to QSAR methods and applications - Follows the Organization for Economic Co-operation and Development principles for QSAR model development - Discusses related techniques such as structure-based design and the combination of structure- and ligand-based design tools

Dr. Kunal Roy is a Professor and Ex-Head in the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India. He has been a recipient of Commonwealth Academic Staff Fellowship (University of Manchester, 2007) and Marie Curie International Incoming Fellowship (University of Manchester, 2013). The field of his research interest is QSAR and Molecular Modeling with application in Drug Design and Ecotoxicological Modeling. Dr. Roy has published more than 350 research articles in refereed journals (current SCOPUS h index 49). He has also coauthored two QSAR-related books, edited six QSAR books and published more than ten book chapters. Dr. Roy is a Co-Editor-in-Chief of Molecular Diversity (Springer Nature). He also serves as a member of the Editorial Boards of several International Journals.

Autoren/Hrsg.

Roy, Kunal

Kar, Supratik

Das, Rudra Narayan

Inhaltsverzeichnis

1;Front Cover;1
2;Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment;4
3;Copyright Page;5
4;Dedication;6
5;Contents;8
6;Foreword;12
7;Preface;14
8;1 Background of QSAR and Historical Developments;16
8.1;1.1 Introduction;17
8.2;1.2 Physicochemical Aspects of Biological Activity of Drugs and Chemicals;21
8.2.1;1.2.1 Hydrophobicity;21
8.2.2;1.2.2 Electronic effect;21
8.2.3;1.2.3 Steric effect;22
8.2.4;1.2.4 Forces and chemical bonding;22
8.2.4.1;1.2.4.1 Covalent bond;22
8.2.4.2;1.2.4.2 Ionic bond;23
8.2.4.3;1.2.4.3 Hydrogen bond;23
8.2.4.4;1.2.4.4 Hydrophobic force;25
8.2.4.5;1.2.4.5 van der Waals interaction;26
8.2.4.6;1.2.4.6 Pi–pi (p–p) stacking interaction;27
8.2.4.7;1.2.4.7 Charge transfer complex;29
8.2.4.8;1.2.4.8 Orbital-overlapping interaction;31
8.2.4.9;1.2.4.9 Ion-dipole and ion-induced dipole interaction;31
8.2.5;1.2.5 Structural features influencing response of chemicals;31
8.2.5.1;1.2.5.1 Stereochemical features influencing drug activity;32
8.2.5.2;1.2.5.2 Isosterism features influencing drug activity;32
8.2.5.3;1.2.5.3 Miscellaneous contribution of structural features;34
8.3;1.3 Structure–Activity Relationship;35
8.3.1;1.3.1 Ideology;35
8.3.2;1.3.2 The components and principal steps involved;38
8.3.3;1.3.3 Naming of the components;41
8.3.4;1.3.4 Objectives of QSAR model development;43
8.3.4.1;1.3.4.1 Prediction of activity/property/toxicity;43
8.3.4.2;1.3.4.2 Reduction and replacement of experimental (laboratory) animals;44
8.3.4.3;1.3.4.3 Virtual screening of library data;44
8.3.4.4;1.3.4.4 Diagnosis of mechanism;45
8.3.4.5;1.3.4.5 Classification of data;45
8.3.4.6;1.3.4.6 Optimization of leads;45
8.3.4.7;1.3.4.7 Refinement of synthetic targets;46
8.4;1.4 Historical Development of QSARs: A Journey of Knowledge Enrichment;46
8.5;1.5 Applications of QSAR;50
8.6;1.6 Regulatory Perspectives of QSAR;53
8.7;1.7 Overview and Conclusion;54
8.8;References;58
9;2 Chemical Information and Descriptors;62
9.1;2.1 Introduction;63
9.2;2.2 Concept of Descriptors;63
9.3;2.3 Type of Descriptors;68
9.3.1;2.3.1 Substituent constants;68
9.3.2;2.3.2 Whole molecular descriptors;69
9.4;2.4 Descriptors Commonly Used in QSAR Studies;69
9.4.1;2.4.1 Physicochemical descriptors;69
9.4.1.1;2.4.1.1 Hydrophobic parameters;69
9.4.1.1.1;2.4.1.1.1 Partition coefficient (log P);69
9.4.1.1.2;2.4.1.1.2 Hydrophobic substituent constant (p);70
9.4.1.1.3;2.4.1.1.3 Hydrophobic fragmental constant (f, f');71
9.4.1.2;2.4.1.2 Electronic parameters;71
9.4.1.2.1;2.4.1.2.1 Acid dissociation constant;71
9.4.1.2.2;2.4.1.2.2 Hammett constant;72
9.4.1.3;2.4.1.3 Steric parameters;73
9.4.1.3.1;2.4.1.3.1 Taft steric constant;73
9.4.1.3.2;2.4.1.3.2 Charton’s steric parameter (.) and van der Waals radius;73
9.4.1.3.3;2.4.1.3.3 Effective Charton’s steric parameter (.ef);74
9.4.1.3.4;2.4.1.3.4 STERIMOL parameters;74
9.4.1.3.5;2.4.1.3.5 Molar refractivity;75
9.4.1.3.6;2.4.1.3.6 Parachor;76
9.4.2;2.4.2 Topological descriptors;76
9.4.3;2.4.3 Structural descriptors;76
9.4.4;2.4.4 Indicator variables;76
9.4.5;2.4.5 Thermodynamic descriptors;82
9.4.6;2.4.6 Electronic parameters;83
9.4.7;2.4.7 Quantum chemical descriptors;84
9.4.7.1;2.4.7.1 Mulliken atomic charges;84
9.4.7.2;2.4.7.2 Quantum topological molecular similarity indices;84
9.4.8;2.4.8 Spatial parameters;84
9.4.8.1;2.4.8.1 RadofGyration;85
9.4.8.2;2.4.8.2 Jurs descriptors;85
9.4.8.3;2.4.8.3 Shadow indices;86
9.4.8.4;2.4.8.4 Molecular surface area;86
9.4.8.5;2.4.8.5 Density;87
9.4.8.6;2.4.8.6 Principal moment of inertia;88
9.4.8.7;2.4.8.7 Molecular volume;88
9.4.9;2.4.9 Information indices;88
9.4.9.1;2.4.9.1 Information of atomic composition index;89
9.4.9.2;2.4.9.2 Information indices based on the A-matrix;89
9.4.9.3;2.4.9.3 Information indices based on the D-matrix;89
9.4.9.4;2.4.9.4 Information indices based on the E-matrix and the ED-matrix;90
9.4.9.5;2.4.9.5 Multigraph information content indices (IC, BIC, CIC, SIC);90
9.4.10;2.4.10 Molecular shape analysis descriptors;90
9.4.11;2.4.11 Molecular field analysis parameters;91
9.4.12;2.4.12 Receptor surface analysis parameters;91
9.5;2.5 Overview and Conclusion;93
9.6;References;93
10;3 Classical QSAR;96
10.1;3.1 Introduction;96
10.2;3.2 The Free–Wilson Model;97
10.2.1;3.2.1 The concept;97
10.2.2;3.2.2 The methodology;97
10.2.3;3.2.3 Example of Free–Wilson model development;98
10.3;3.3 The Fujita–Ban Model;103
10.3.1;3.3.1 The concept;103
10.3.2;3.3.2 The methodology;104
10.4;3.4 The LFER Model;107
10.4.1;3.4.1 The concept;107
10.4.2;3.4.2 Genesis;107
10.4.3;3.4.3 An example;111
10.4.4;3.4.4 Applications;113
10.5;3.5 Kubinyi’s Bilinear Model;113
10.6;3.6 The Mixed Approach;115
10.7;3.7 Overview and Conclusions;116
10.8;References;116
11;4 Topological QSAR;118
11.1;4.1 Introduction;118
11.2;4.2 Topology: A Method of Chemical Structure Representation;119
11.3;4.3 Graphs and Matrices: Platforms for the Topological Paradigm;120
11.3.1;4.3.1 Graph theory and chemical graphs;120
11.3.2;4.3.2 Matrix: aiding the numerical presentation of graph theory;123
11.4;4.4 Topological Indices;136
11.4.1;4.4.1 The context and formalism;136
11.4.2;4.4.2 Wiener, Platt, Hosoya, Zagreb, and Balaban indices;137
11.4.2.1;4.4.2.1 Wiener index (W);137
11.4.2.2;4.4.2.2 Platt number (F);138
11.4.2.3;4.4.2.3 Hosoya index (Z);138
11.4.2.4;4.4.2.4 Zagreb index;138
11.4.2.5;4.4.2.5 Balaban index (J);140
11.4.3;4.4.3 Molecular connectivity indices;140
11.4.3.1;4.4.3.1 Randic connectivity index;141
11.4.3.2;4.4.3.2 Kier and Hall’s connectivity index;142
11.4.4;4.4.4 Kappa shape indices;146
11.4.5;4.4.5 Electrotopological state (E-state) indices;152
11.4.6;4.4.6 Extended topochemical atom indices;154
11.5;4.5 Conclusion and Possibilities;155
11.6;References;162
12;5 Computational Chemistry;166
12.1;5.1 Introduction;166
12.2;5.2 Computer Use in Chemistry;167
12.2.1;5.2.1 Visualization;168
12.2.1.1;5.2.1.1 Structure drawing;168
12.2.1.2;5.2.1.2 3D visualization;169
12.2.1.3;5.2.1.3 Visualization of ligand–receptor interactions;170
12.2.2;5.2.2 Calculation and simulation;170
12.2.3;5.2.3 Analysis and storage of data;171
12.3;5.3 Conformational Analysis and Energy Minimization;173
12.3.1;5.3.1 The concept;173
12.3.2;5.3.2 Conformational search;175
12.3.3;5.3.3 Minimization of energy;175
12.4;5.4 Molecular Mechanics;180
12.5;5.5 Molecular Dynamics;185
12.5.1;5.5.1 Definition;185
12.5.2;5.5.2 Development and components;186
12.5.3;5.5.3 The algorithm;187
12.6;5.6 Quantum Mechanics;188
12.6.1;5.6.1 The Born–Oppenheimer approximation;194
12.6.2;5.6.2 The Hartree–Fock approximation;194
12.6.3;5.6.3 Density functional theory;195
12.6.4;5.6.4 Semiempirical analysis;196
12.6.4.1;5.6.4.1 Concept;196
12.6.4.2;5.6.4.2 Developmental background;197
12.6.4.3;5.6.4.3 Modified neglect of diatomic overlap;197
12.6.4.4;5.6.4.4 Austin model 1;199
12.6.4.5;5.6.4.5 Parametric method 3;199
12.6.4.6;5.6.4.6 PDDG/PM3 and PDDG/MNDO;200
12.7;5.7 Overview and Conclusion;200
12.8;References;203
13;6 Selected Statistical Methods in QSAR;206
13.1;6.1 Introduction;206
13.2;6.2 Regression-Based Approaches;207
13.2.1;6.2.1 Multiple linear regression;207
13.2.1.1;6.2.1.1 Model development;207
13.2.1.2;6.2.1.2 Statistical metrics to examine the quality of the developed model;211
13.2.1.2.1;6.2.1.2.1 Mean average error;211
13.2.1.2.2;6.2.1.2.2 Determination coefficient (R2);211
13.2.1.2.3;6.2.1.2.3 Adjusted R2 (Ra2);212
13.2.1.2.4;6.2.1.2.4 Variance ratio (F);212
13.2.1.2.5;6.2.1.2.5 Standard error of estimate (s);213
13.2.1.2.6;6.2.1.2.6 Root mean square error of calibration;213
13.2.1.2.7;6.2.1.2.7 The “t” test for each regression coefficient;213
13.2.1.2.8;6.2.1.2.8 Intercorrelation among descriptors;214
13.2.1.3;6.2.1.3 Example of an MLR model development;214
13.2.1.4;6.2.1.4 Data pretreatment and variable selection;222
13.2.1.4.1;6.2.1.4.1 Data set curation;222
13.2.1.4.2;6.2.1.4.2 Data pretreatment;223
13.2.1.4.3;6.2.1.4.3 Variable selection (feature selection);223
13.2.1.4.3.1;6.2.1.4.3.1 Stepwise selection;223
13.2.1.4.3.2;6.2.1.4.3.2 All possible subset selection;224
13.2.1.4.3.3;6.2.1.4.3.3 Genetic method;224
13.2.1.4.3.4;6.2.1.4.3.4 Factor analysis;225
13.2.1.4.3.5;6.2.1.4.3.5 Other methods;225
13.2.1.4.3.5.1;Particle swarm optimization;225
13.2.1.4.3.5.2;Ant colony optimization;226
13.2.1.4.3.5.3;k-Nearest neighborhood method;226
13.2.2;6.2.2 Partial least squares;226
13.2.2.1;6.2.2.1 The method;227
13.2.2.2;6.2.2.2 An example;228
13.2.3;6.2.3 Principal component regression analysis;229
13.2.4;6.2.4 Ridge regression;230
13.3;6.3 Classification-Based QSAR;230
13.3.1;6.3.1 Linear discriminant analysis;231
13.3.2;6.3.2 Logistic regression;231
13.3.3;6.3.3 Cluster analysis;233
13.3.3.1;6.3.3.1 Hierarchical cluster analysis;234
13.3.3.2;6.3.3.2 k-Means clustering;234
13.4;6.4 Machine Learning Techniques;235
13.4.1;6.4.1 Artificial neural network;235
13.4.2;6.4.2 Bayesian neural network;240
13.4.3;6.4.3 Decision tree and random forest;241
13.4.4;6.4.4 Support vector machine;242
13.5;6.5 Conclusion;243
13.6;References;243
14;7 Validation of QSAR Models;246
14.1;7.1 Introduction;246
14.2;7.2 Different Validation Methods;248
14.2.1;7.2.1 The OECD principles;249
14.2.2;7.2.2 Internal validation;251
14.2.3;7.2.3 External validation;251
14.2.3.1;7.2.3.1 Division of the data set into training and test sets;251
14.2.3.2;7.2.3.2 Applicability domain;254
14.2.3.2.1;7.2.3.2.1 Concept of the AD;254
14.2.3.2.2;7.2.3.2.2 History behind the introduction of the AD;254
14.2.3.2.3;7.2.3.2.3 Types of AD approaches;255
14.2.3.2.4;7.2.3.2.4 Checklist and importance of the AD study in validation;268
14.2.4;7.2.4 Validation metrics;269
14.2.4.1;7.2.4.1 Validation metrics for regression-based QSAR models;269
14.2.4.1.1;7.2.4.1.1 Metrics for internal validation;269
14.2.4.1.1.1;7.2.4.1.1.1 Leave-one-out cross-validation;269
14.2.4.1.1.2;7.2.4.1.1.2 Leave-many-out cross-validation;273
14.2.4.1.1.3;7.2.4.1.1.3 True Q2;273
14.2.4.1.1.4;7.2.4.1.1.4 The r2m metric for internal validation;274
14.2.4.1.1.5;7.2.4.1.1.5 True r2m(LOO);277
14.2.4.1.1.6;7.2.4.1.1.6 Bootstrapping;277
14.2.4.1.1.7;7.2.4.1.1.7 Metrics for chance correlation: Y-randomization;277
14.2.4.1.2;7.2.4.1.2 Metrics for external validation;278
14.2.4.1.2.1;7.2.4.1.2.1 Predictive R2 (R2pred);278
14.2.4.1.2.2;7.2.4.1.2.2 Validation based on Golbraikh and Tropsha’s criteria;278
14.2.4.1.2.3;7.2.4.1.2.3 The r2m(test) metric for external validation;279
14.2.4.1.2.4;7.2.4.1.2.4 RMSEP;280
14.2.4.1.2.5;7.2.4.1.2.5 Q2(F2);280
14.2.4.1.2.6;7.2.4.1.2.6 Q2(F3);280
14.2.4.1.2.7;7.2.4.1.2.7 Concordance correlation coefficient;281
14.2.4.1.2.8;7.2.4.1.2.8 The r2m(rank) metric;281
14.2.4.2;7.2.4.2 Validation metrics for classification-based QSAR models;282
14.2.4.2.1;7.2.4.2.1 Goodness-of-fit and quality measures;282
14.2.4.2.1.1;7.2.4.2.1.1 Wilks lambda (.) statistics;282
14.2.4.2.1.2;7.2.4.2.1.2 Canonical index (Rc);283
14.2.4.2.1.3;7.2.4.2.1.3 Chi-square (.2);283
14.2.4.2.1.4;7.2.4.2.1.4 Squared Mahalanobis distance;283
14.2.4.2.2;7.2.4.2.2 Metrics for model performance parameters;283
14.2.4.2.2.1;7.2.4.2.2.1 Sensitivity, specificity, and accuracy;283
14.2.4.2.2.2;7.2.4.2.2.2 F-measure and precision;284
14.2.4.2.2.3;7.2.4.2.2.3 G-means;284
14.2.4.2.2.4;7.2.4.2.2.4 Cohen’s .;285
14.2.4.2.2.5;7.2.4.2.2.5 Matthews correlation coefficient;285
14.2.4.2.3;7.2.4.2.3 Parameters for receiver operating characteristics analysis;286
14.2.4.2.3.1;7.2.4.2.3.1 ROC curve;286
14.2.4.2.3.2;7.2.4.2.3.2 ROCED and ROCFIT;287
14.2.4.2.3.3;7.2.4.2.3.3 AUC-ROC;288
14.2.4.2.4;7.2.4.2.4 Metrics for Pharmacological distribution diagram;288
14.3;7.3 A Practical Example of the Calculation of Common Validation Metrics and the AD;289
14.4;7.4 QSAR model reporting format;299
14.4.1;7.4.1 Concept of the QMRF;299
14.4.2;7.4.2 Why QMRF?;299
14.4.3;7.4.3 How to construct QMRF;300
14.4.4;7.4.4 Utility of the QMRF;300
14.5;7.5 Overview and Conclusion;300
14.6;References;301
15;8 Introduction to 3D-QSAR;306
15.1;8.1 Introduction;307
15.2;8.2 Comparative Molecular Field Analysis;308
15.2.1;8.2.1 Concept of CoMFA;308
15.2.2;8.2.2 Methodology of CoMFA;309
15.2.3;8.2.3 Factors responsible for the performance of CoMFA;310
15.2.3.1;8.2.3.1 Biological data;310
15.2.3.2;8.2.3.2 Optimization of 3D structure of the compounds;311
15.2.3.3;8.2.3.3 Conformational analysis of compounds;312
15.2.3.4;8.2.3.4 Determination of bioactive conformations;313
15.2.3.4.1;8.2.3.4.1 X-ray crystallography;313
15.2.3.4.2;8.2.3.4.2 NMR spectroscopy;314
15.2.3.5;8.2.3.5 Alignment of molecules;314
15.2.3.6;8.2.3.6 Calculation of molecular interaction energy fields;315
15.2.3.7;8.2.3.7 Model generation;315
15.2.4;8.2.4 Display and interpretation of results;316
15.2.5;8.2.5 Advantages and drawbacks of CoMFA;316
15.3;8.3 Comparative Molecular Similarity Indices Analysis;317
15.3.1;8.3.1 Concept of comparative molecular similarity indices analysis;317
15.3.2;8.3.2 Methodology of CoMSIA;317
15.3.3;8.3.3 Advantages of CoMSIA;318
15.4;8.4 Molecular Shape Analysis;319
15.4.1;8.4.1 Concept of molecular shape analysis;319
15.4.2;8.4.2 Methodology of the MSA;320
15.4.3;8.4.3 MSA descriptors;321
15.5;8.5 Receptor Surface Analysis;322
15.5.1;8.5.1 Concept of receptor surface analysis;322
15.5.2;8.5.2 Methodology of the RSA;322
15.5.3;8.5.3 RSA descriptors;322
15.6;8.6 Other Approaches;323
15.6.1;8.6.1 Alignment-based 3D-QSAR models;323
15.6.1.1;8.6.1.1 Self-organizing molecular field analysis;323
15.6.1.2;8.6.1.2 Voronoi field analysis;324
15.6.1.3;8.6.1.3 Molecular quantum similarity measures;325
15.6.1.4;8.6.1.4 Adaptation of the fields for molecular comparison;325
15.6.1.5;8.6.1.5 Genetically evolved receptor modeling;326
15.6.1.6;8.6.1.6 Hint interaction field analysis;327
15.6.2;8.6.2 Alignment-independent 3D-QSAR models;327
15.6.2.1;8.6.2.1 Comparative molecular moment analysis;327
15.6.2.2;8.6.2.2 Weighted holistic invariant molecular descriptor analysis;328
15.6.2.3;8.6.2.3 VolSurf;328
15.6.2.4;8.6.2.4 Compass;328
15.6.2.5;8.6.2.5 GRID;329
15.6.2.6;8.6.2.6 Comparative spectral analysis;330
15.6.2.7;8.6.2.7 Quantum chemical parameters in QSAR analysis;330
15.7;8.7 Overview and Conclusions;330
15.8;References;331
16;9 Newer QSAR Techniques;334
16.1;9.1 Introduction;335
16.2;9.2 HQSAR;336
16.2.1;9.2.1 Concept of HQSAR;336
16.2.2;9.2.2 How to develop an HQSAR model;336
16.2.3;9.2.3 HQSAR parameters;338
16.2.3.1;9.2.3.1 Hologram length;338
16.2.3.2;9.2.3.2 Fragment size;339
16.2.3.3;9.2.3.3 Fragment distinction;339
16.2.4;9.2.4 Why use HQSAR over other techniques?;339
16.2.5;9.2.5 Application of HQSAR models;340
16.2.5.1;9.2.5.1 A flexible tool in drug design;340
16.2.5.2;9.2.5.2 Mathematical correlation to activity/property prediction;342
16.2.5.3;9.2.5.3 Pharmacokinetic studies and ADME prediction;343
16.3;9.3 G-QSAR;344
16.3.1;9.3.1 Concept of G-QSAR;344
16.3.2;9.3.2 Background of evaluation of G-QSAR method;344
16.3.3;9.3.3 G-QSAR methodology;345
16.3.3.1;9.3.3.1 Molecular fragmentation;346
16.3.3.2;9.3.3.2 Calculation of fragment descriptors;347
16.3.3.3;9.3.3.3 G-QSAR model development;347
16.3.4;9.3.4 Application of the G-QSAR model;347
16.3.4.1;9.3.4.1 NCE design based on fragments;348
16.3.4.2;9.3.4.2 Scaffold hopping and lead optimization;349
16.3.4.3;9.3.4.3 Addressing the inverse QSAR problem;349
16.3.4.4;9.3.4.4 Mathematical correlation to activity prediction;350
16.4;9.4 Other Approaches;351
16.4.1;9.4.1 MIA-QSAR;351
16.4.1.1;9.4.1.1 Concept of MIA-QSAR;351
16.4.1.2;9.4.1.2 Methodology of MIA-QSAR;352
16.4.1.2.1;9.4.1.2.1 Descriptor calculation;352
16.4.1.2.2;9.4.1.2.2 Model development;352
16.4.1.3;9.4.1.3 Pros and cons of MIA-QSAR;353
16.4.1.4;9.4.1.4 Application of MIA-QSAR;354
16.4.2;9.4.2 Binary QSAR;355
16.4.2.1;9.4.2.1 Concept of binary QSAR;355
16.4.2.2;9.4.2.2 Methodology of binary QSAR;355
16.4.3;9.4.3 Fragment-based QSAR;356
16.4.4;9.4.4 Fragment-similarity-based QSAR;357
16.4.5;9.4.5 Ensemble QSAR;358
16.4.5.1;9.4.5.1 Concept of eQSAR;358
16.4.5.2;9.4.5.2 Importance and application of eQSAR;359
16.4.6;9.4.6 LQTA-QSAR;359
16.4.6.1;9.4.6.1 Concept of LQTA-QSAR;359
16.4.6.2;9.4.6.2 Methodology of LQTA-QSAR;360
16.4.7;9.4.7 SOM 4D-QSAR;360
16.4.8;9.4.8 Receptor-independent 4D-QSAR;362
16.4.9;9.4.9 Receptor-dependent 4D-QSAR;364
16.4.10;9.4.10 5D-QSAR (QUASAR);367
16.4.11;9.4.11 6D-QSAR;367
16.4.12;9.4.12 7D-QSAR;368
16.5;9.5 Overview and Conclusions;368
16.6;References;369
17;10 Other Related Techniques;372
17.1;10.1 Introduction;373
17.2;10.2 Pharmacophore;374
17.2.1;10.2.1 Concept and definition;374
17.2.2;10.2.2 Background and early days of pharmacophore;375
17.2.3;10.2.3 Methodology of pharmacophore mapping;376
17.2.3.1;10.2.3.1 Diverse conformation generation;376
17.2.3.2;10.2.3.2 Generation of 3D pharmacophore;377
17.2.3.3;10.2.3.3 Assessment of the quality of pharmacophore hypotheses;378
17.2.3.4;10.2.3.4 Validation of the pharmacophore model;380
17.2.4;10.2.4 Types of pharmacophore;381
17.2.4.1;10.2.4.1 Ligand-based pharmacophore modeling;381
17.2.4.2;10.2.4.2 Structure-based pharmacophore modeling;383
17.2.5;10.2.5 Application of pharmacophore models;387
17.2.5.1;10.2.5.1 Pharmacophore model–based VS;387
17.2.5.2;10.2.5.2 Pharmacophore-based de novo design;388
17.2.6;10.2.6 Advantages and limitations of pharmacophore;389
17.2.7;10.2.7 Software tools for pharmacophore analysis;390
17.3;10.3 Structure-Based Design–Docking;390
17.3.1;10.3.1 Concept and definition of docking;390
17.3.2;10.3.2 Definition of fundamental terms of docking;394
17.3.3;10.3.3 Essential requirements of docking;396
17.3.4;10.3.4 Categorization of docking;397
17.3.4.1;10.3.4.1 Receptor/protein flexibility;398
17.3.4.1.1;10.3.4.1.1 Soft docking;398
17.3.4.1.2;10.3.4.1.2 Side-chain flexibility;399
17.3.4.1.3;10.3.4.1.3 Molecular relaxation;400
17.3.4.1.4;10.3.4.1.4 Docking of multiple protein structures/ensemble docking;400
17.3.4.2;10.3.4.2 Ligand sampling and flexibility;400
17.3.4.2.1;10.3.4.2.1 Shape matching;401
17.3.4.2.2;10.3.4.2.2 Systematic search;401
17.3.4.2.3;10.3.4.2.3 Stochastic algorithms;402
17.3.4.3;10.3.4.3 Docking scoring functions;402
17.3.4.3.1;10.3.4.3.1 FF scoring functions;402
17.3.4.3.2;10.3.4.3.2 Empirical scoring functions;402
17.3.4.3.3;10.3.4.3.3 Knowledge-based scoring functions;403
17.3.4.3.4;10.3.4.3.4 Consensus scoring;403
17.3.4.3.5;10.3.4.3.5 Clustering- and entropy-based scoring methods;403
17.3.5;10.3.5 Basic steps of docking;403
17.3.6;10.3.6 Challenges and required improvements in docking studies;404
17.3.7;10.3.7 Applications of docking;407
17.3.8;10.3.8 Docking software tools;408
17.4;10.4 Combination of Structure- and Ligand-Based Design Tools;411
17.4.1;10.4.1 Comparative binding energy analysis;411
17.4.1.1;10.4.1.1 The concept of comparative binding energy;411
17.4.1.2;10.4.1.2 The methodology of COMBINE;412
17.4.1.3;10.4.1.3 Importance and advantages of COMBINE;414
17.4.1.4;10.4.1.4 Drawbacks and required improvements;414
17.4.1.5;10.4.1.5 Applications of COMBINE;414
17.4.1.6;10.4.1.6 Software for COMBINE;415
17.4.2;10.4.2 Comparative residue interaction analysis;415
17.4.2.1;10.4.2.1 Concept of CoRIA;415
17.4.2.2;10.4.2.2 Methodology of CoRIA;416
17.4.2.3;10.4.2.3 Variants of CoRIA;416
17.4.2.4;10.4.2.4 Importance and application of CoRIA;417
17.4.2.5;10.4.2.5 Drawback of CoRIA;417
17.4.2.6;10.4.2.6 Future perspective of CoRIA;418
17.5;10.5 In silico Screening of Chemical Libraries: VS;418
17.5.1;10.5.1 Concept;418
17.5.2;10.5.2 Workflow and types of VS;418
17.5.2.1;10.5.2.1 Selection of chemical libraries/databases;419
17.5.2.2;10.5.2.2 Preprocessing of chemical libraries;419
17.5.2.3;10.5.2.3 Filtering of druglike molecules;419
17.5.2.4;10.5.2.4 Screening;419
17.5.2.5;10.5.2.5 Hit selection to new chemical entity generation;420
17.5.3;10.5.3 Successful application of VS: A few case studies;421
17.5.4;10.5.4 Advantages of VS;421
17.5.4.1;10.5.4.1 Cost-effective;428
17.5.4.2;10.5.4.2 Time-saving;428
17.5.4.3;10.5.4.3 Labor-efficient;428
17.5.4.4;10.5.4.4 Sensible alternative;428
17.5.5;10.5.5 Pitfalls;428
17.5.6;10.5.6 Databases for the VS;431
17.6;10.6 Overview and Conclusions;432
17.7;References;436
18;11 SAR and QSAR in Drug Discovery and Chemical Design—Some Examples;442
18.1;11.1 Introduction;442
18.2;11.2 Successful Applications of QSAR and Other In Silico Methods: Representative Examples;443
18.2.1;11.2.1 Examples of some approved drugs;443
18.2.2;11.2.2 Examples of other approved chemicals;459
18.2.3;11.2.3 Examples of investigational drugs at different phases of current clinical trials;461
18.3;11.3 Conclusion;465
18.4;References;465
19;12 Future Avenues;470
19.1;12.1 Introduction;470
19.2;12.2 Application Areas;471
19.2.1;12.2.1 QSAR of mixture toxicity;471
19.2.2;12.2.2 Peptide QSAR;471
19.2.3;12.2.3 QSAR of nanoparticles;471
19.2.4;12.2.4 QSAR of ionic liquids;472
19.2.5;12.2.5 QSAR of cosmetics;473
19.2.6;12.2.6 PKPD-linked QSAR modeling;473
19.2.7;12.2.7 Material informatics;474
19.2.8;12.2.8 Ecotoxicity modeling of pharmaceuticals;474
19.2.9;12.2.9 Interspecies toxicity modeling;475
19.2.10;12.2.10 QSAR of phytochemicals;475
19.3;12.3 Conclusion;475
19.4;References;475
20;Index;478

Leseproben

Chapter 2 Chemical Information and Descriptors
Computational modeling, quantitative structure–activity relationship (QSAR) in particular, plays an important role in the chemistry disciplines ranging from drug discovery to materials science. Numerical portrayal of molecular structures encoding the required chemical information responsible for a given molecular property (or activity) is the first step in a QSAR analysis. This numerical depiction of molecular structure information is carried out through computation of descriptors, which can be considered in many forms, from simple atom counts to complex molecular features. Molecular descriptors play a fundamental role in cheminformatics and chemometric analyses. The concept of chemical information and descriptors, as well as a broad categorization of descriptors, are highlighted in this chapter. Keywords
Descriptors; graph theory; physicochemical; electronic; structural; topological; quantum Contents 2.1 Introduction 48 2.2 Concept of Descriptors 48 2.3 Type of Descriptors 53 2.3.1 Substituent constants 53 2.3.2 Whole molecular descriptors 54 2.4 Descriptors Commonly Used in QSAR Studies 54 2.4.1 Physicochemical descriptors 54 2.4.1.1 Hydrophobic parameters 54 2.4.1.2 Electronic parameters 56 2.4.1.3 Steric parameters 58 2.4.2 Topological descriptors 61 2.4.3 Structural descriptors 61 2.4.4 Indicator variables 61 2.4.5 Thermodynamic descriptors 67 2.4.6 Electronic parameters 68 2.4.7 Quantum chemical descriptors 69 2.4.7.1 Mulliken atomic charges 69 2.4.7.2 Quantum topological molecular similarity indices 69 2.4.8 Spatial parameters 69 2.4.8.1 RadofGyration 70 2.4.8.2 Jurs descriptors 70 2.4.8.3 Shadow indices 71 2.4.8.4 Molecular surface area 71 2.4.8.5 Density 72 2.4.8.6 Principal moment of inertia 73 2.4.8.7 Molecular volume 73 2.4.9 Information indices 73 2.4.9.1 Information of atomic composition index 74 2.4.9.2 Information indices based on the A-matrix 74 2.4.9.3 Information indices based on the D-matrix 74 2.4.9.4 Information indices based on the E-matrix and the ED-matrix 75 2.4.9.5 Multigraph information content indices (IC, BIC, CIC, SIC) 75 2.4.10 Molecular shape analysis descriptors 75 2.4.11 Molecular field analysis parameters 76 2.4.12 Receptor surface analysis parameters 76 2.5 Overview and Conclusion 78 References 78 2.1 Introduction
The quantitative structure–activity relationship (QSAR) technique, being directly related to the molecular structures of chemicals, can explain the effects exerted by the chemicals in relation to their structures and properties. Any significant search for the required chemical information of molecules for a particular end point can provide a strong tool for the predictive assessment of the response of existing untested as well as new chemicals [1]. QSAR is a simple mathematical model that can correlate chemistry with the properties (physicochemical/biological/toxicological) of molecules using various computationally or experimentally derived quantitative parameters known as descriptors. These descriptors are correlated with the response variable using a variety of chemometric tools in order to obtain a meaningful QSAR model. The developed models provide a significant insight regarding the essential structural requisites of the molecules, thus enabling us to identify the features contributing to the biological activity/property/toxicity of the studied molecules [2]. 2.2 Concept of Descriptors
Molecular descriptors are terms that characterize specific information about a studied molecule. They are the “numerical values associated with the chemical constitution for correlation of chemical structure with various physical properties, chemical reactivity, or biological activity” [3,4]. In other words, the modeled response (activity/property/toxicity of query molecules) is represented as a function of quantitative values of structural features or properties that are termed as descriptors for a QSAR model. Cheminformatics methods depend on the generation of chemical reference spaces into which new chemical entities are predictable by the developed QSAR model. The definition of chemical spaces significantly depends on the use of computational descriptors of studied molecular structure, physical or chemical properties, or specific features. (activity/property/toxicity)=f(Information in form of chemical structureor property)=f(Descriptors) The type of descriptors used and the extent to which they can encode the structural features of the molecules that are correlated to the response are critical determinants of the quality of any QSAR model. The descriptors may be physicochemical (hydrophobic, steric, or electronic), structural (based on frequency of occurrence of a substructure), topological, electronic (based on molecular orbital calculations), geometric (based on a molecular surface area calculation), or simple indicator parameters (dummy variables). A schematic overview is presented in Figure 2.1 in order to show the steps how a chemical structure is used to calculate descriptors and used in QSAR model development.
Figure 2.1 How chemical structure is used to calculate descriptors and QSAR model development. A dimension in the QSAR analysis acts as the constraint that controls the nature of the analysis. The term dimension in predictive model development is roughly associated with the complexity of the modeling technique that directly signifies the degree of descriptors. The dimension of an object can be mathematically attributed to the minimum number of coordinates needed for specifying a particular point in it [1]. The addition of dimension to a specific geometric object assists in identifying it in a different way by adding more information. Thus, it is clear that dimension is an intrinsic property of an object and does not depend on the space of the object [1]. The addition of new dimensions to the QSAR technique helps in deriving structural information at a higher level of analysis. With the use of ascending dimensions of descriptors in the modern QSAR analysis, a QSAR modeler may be able to reveal new features of the molecules. The dimensionality of descriptors depends on the type of algorithm employed and defines the nature of QSAR analysis. In the development of a predictive model, the dimension is assigned on the basis of the nature of the independent variables (descriptors) and the corresponding QSAR modeling is named likewise; that is, a QSAR model comprising of one-dimensional (1D) parameters is called 1D-QSAR. In other words, one can conclude that the dimension of the performed QSAR analysis follows the dimension of the descriptor. In order to pursue a quantitative analysis on structure of chemical compounds, generation of data encoding chemical information is an essential first step in the development of the QSAR model. It is therefore envisaged that QSAR analysis attempts to develop predictive models in the form of mathematical relations by using chemical information about molecules. Descriptors represent the chemical information that encodes the behavior of a molecular entity. They are the numerical or quantitative representations of chemical compounds derived using suitable algorithms and are used as independent variables for predictive model development. In summary, any apt structural information quantitatively describing the biological activity/property/toxicity of a molecule can be defined as a descriptor. Hence, molecular descriptors range from simple atomic counts or molecular weight measures to complex spatial or geometrical features [5]. One can describe a single molecule in many ways. It is possible to compute thousands of numerical descriptors for a given chemical. Many of these descriptors are very closely related to each other and even capture the same information at times. Thus, the selection of relevant descriptors is a well-known problem, and it requires a lot of experience for the QSAR modeler to select the appropriate ones for the model development [6]. In addition, one has to take into account the nature of the chemical structure being considered. A set of descriptors may efficiently encode the chemical information perfectly for the small molecules, but the same set of descriptors may not be able to encode the required features for polymers, protein structures, and inorganic molecules. Thus, not only the calculation...