Comprehensive Medicinal Chemistry II, Volume 1

Volume 4 : Computer-Assisted Drug Design......Page 1
Applications to Drug Discovery - Ligand-Based Lead Optimization......Page 2
Introduction to the Volume......Page 4
Computer-Assisted Drug Design and the Evolving Drug Discovery Process......Page 7
Partnership with Structural Biology - A Case History......Page 9
Future Challenges and Perspectives......Page 13
References......Page 14
Introduction......Page 15
Historical Evolution......Page 16
Active-Analog Approach......Page 17
Active-Site Modeling......Page 19
Statistical Modeling......Page 20
Protein Structure Prediction......Page 21
Docking/Prediction of Affinity......Page 23
Scaffold Development......Page 24
Protein Engineering......Page 26
Reducing Entropy of the System......Page 28
Developing alpha-Helical Scaffolds......Page 30
Combinatorial Chemistry and High-Throughput Screening......Page 33
Privileged Organic Scaffolds......Page 34
Diversity and Similarity......Page 36
Summary and Perspective for the Future......Page 37
References......Page 38
4.03 Quantitative Structure-Activity Relationship - A Historical Perspective and the Future......Page 44
Electronic Effects......Page 45
Steric Effects......Page 47
Hydrophobicity to the Rescue......Page 48
Experimental Methods of Determining Partition Coefficients......Page 49
Calculations Based on Whole-Molecule Properties......Page 50
Free and Wilson’s de novo Contributions......Page 51
Kubinyi’s Model......Page 52
Advent of Molecular Graphics......Page 53
New Quantitative Structure-Activity Relationship Approaches......Page 54
Inverse Quantitative Structure-Activity Relationship (Inverse QSAR)......Page 55
Hydrogen Bond Descriptors......Page 56
Quantum Chemical Indices......Page 57
Penetration of Drugs through the Blood-Brain Barrier......Page 58
Future Perspectives......Page 59
References......Page 60
4.04 Structure-Based Drug Design - A Historical Perspective and the Future......Page 65
X-ray crystal data collection......Page 66
Molecular models......Page 67
Molecular modeling/computer-aided drug discovery......Page 68
History......Page 69
Sickle-cell anemia......Page 70
Hemoglobin as a model drug receptor, as a model for structure-based design of allosteric effectors, and as a model for other important allosteric proteins......Page 71
Structure-guided optimization......Page 72
Reiterative design: glycinamide ribonucleotide formyl-transferase......Page 74
The First 10 Years at Agouron......Page 76
High-Throughput Crystallography......Page 80
Real-Time Crystallography......Page 81
References......Page 82
4.05 Ligand-Based Approaches: Core Molecular Modeling......Page 87
Molecular Mechanics and Empirical Force Field Methods......Page 88
Bond Stretching and Compressing......Page 89
Torsion Potentials......Page 90
Out-of-Plane Bending Terms and Cross Terms......Page 91
Simple Models for Incorporating Electrostatic Interactions......Page 92
The Dielectric Constant and Implicit Solvation Models......Page 94
The Generalized Born Model......Page 95
van der Waals’ Interactions......Page 96
Comparison and Practical Application of Force Fields......Page 98
Potential Energy Surfaces......Page 99
Energy Minimization......Page 101
Applications of Energy Minimization: Normal Mode Analysis......Page 103
Systematic Search Methods......Page 104
Distance Geometry......Page 105
Genetic and Evolutionary Algorithms......Page 106
Generating a Set of ’Representative’ Conformations......Page 107
Comparisons and Evaluations of Conformational Search Methods......Page 109
Three-Dimensional Structure Generation......Page 110
Three-Dimensional Molecular Alignment......Page 111
Field-Based Alignment Methods......Page 112
Comparison and Evaluation of Similarity Methods......Page 113
References......Page 114
4.06 Pharmacophore Modeling: 1 - Methods......Page 119
Overview......Page 120
Selection of Compounds to Include......Page 121
Shape features......Page 124
Treatment of Conformational Flexibility......Page 125
Techniques to Describe the Molecule and its Pharmacophore Features to the Computer......Page 126
Ensemble distance geometry......Page 128
Clique detection......Page 132
Statistical classification of activities of molecules for pharmacophore identification......Page 133
Strategies to Evaluate a Pharmacophore Hypothesis......Page 134
Limitations of the Assumptions for Pharmacophore Mapping......Page 137
Sources of Compounds......Page 138
Geometric searching......Page 141
Unresolved Challenges......Page 142
References......Page 143
Quantitative Structure-Activity Relationship (QSAR) Modeling in Modern Medicinal Chemistry......Page 148
Key Quantitative Structure-Activity Relationship Concepts......Page 150
Molecular Descriptors......Page 151
Topological Descriptors......Page 152
General Classification......Page 154
Correlation Approaches......Page 155
Y-Randomization......Page 156
Rational Division of Available Data Sets into Training and Test Sets......Page 157
Similarity distance......Page 158
Validated Quantitative Structure-Activity Relationship Models as Virtual Screening Tools......Page 159
References......Page 162
Similarity Searching......Page 165
Whole-molecule properties......Page 167
Two-dimensional descriptors......Page 168
Three-dimensional descriptors......Page 170
Similarity Coefficients......Page 172
Comparison of Similarity Measures......Page 177
Combination of Similarity Measures......Page 180
Diversity......Page 181
Clustering......Page 182
Partitioning......Page 183
Dissimilarity-Based Compound Selection......Page 184
Optimization Techniques......Page 185
References......Page 186
General Principles......Page 191
Steric......Page 193
Electrostatic......Page 194
Hydrogen Bonds......Page 197
Entropy......Page 199
General Concepts......Page 200
Torsion angle rotations......Page 201
Large-scale (low-frequency) domain motions in proteins......Page 202
Current Models of Flexible Receptor-Ligand Binding......Page 203
Selected rotational degrees of freedom......Page 204
Ensembles of multiple structures......Page 205
Water molecules......Page 206
References......Page 207
4.10 Comparative Modeling of Drug Target Proteins......Page 212
The Basis of Comparative Modeling......Page 213
Steps in Comparative Modeling......Page 214
Sequence-sequence methods......Page 215
Alignment Errors are Unrecoverable......Page 216
Relative Accuracy, Flexibility, and Automation......Page 217
Optimization-based methods......Page 218
Errors in Regions without a Template......Page 219
Errors in Side-Chain Packing......Page 220
Applications of Comparative Models......Page 221
Comparative Models versus Experimental Structures in Virtual Screening......Page 223
Use of Comparative Models to Obtain Novel Drug Leads......Page 224
Automation and Availability of Resources for Comparative Modeling and Ligand Docking......Page 225
References......Page 228
Introduction......Page 234
Calculation of Molecular Interaction Fields: The Target......Page 235
Calculation of Molecular Interaction Fields: The Probe......Page 236
The GRID Force Field......Page 237
Applications of GRID Molecular Interaction Fields......Page 238
Determination of Water Positions......Page 239
Comparison of Hydrophobic Patches......Page 241
Identification of Protein-Binding Sites......Page 242
Aliphatic amidine multiatom probe......Page 243
GRID Molecular Interaction Fields for a Ligand Molecule......Page 244
Competition between Probe and Water......Page 245
References......Page 248
Introduction......Page 251
Docking Programs and Algorithms......Page 252
Docking Programs: GOLD......Page 253
Docking Programs: Quick Explore (QXP)......Page 255
Developing and Optimizing Scoring Functions......Page 256
Target-based tuning: three-way partial least-square (PLS)......Page 257
Rescoring......Page 258
Physics-based scoring: molecular mechanics-Poisson-Boltzmann solvent-accessible surface area (MM-PBSA)......Page 259
Composite scoring functions......Page 260
Assessment of Docking Performance......Page 261
Pose Prediction......Page 262
Data sets......Page 263
Docking into Flexible Receptors or the Cross-Docking Problem......Page 264
Water......Page 266
Docking with Constraints......Page 267
Docking Applications and Successes......Page 268
Inhibiting b-Catenin-Tcf Protein-Protein Interactions......Page 269
Virtual and Experimental Screening of Protein Tyrosine Phosphatase-1B......Page 270
Layered Virtual Screening: Application to Carbonic Anhydrase II and Checkpoint Kinase-1......Page 272
References......Page 273
4.13 De Novo Design......Page 278
Overview of De Novo Design......Page 279
Targets......Page 280
Derivation of Interaction Sites using Rules......Page 281
Site Analysis using Docking and Minimization......Page 282
Ring and Acyclic Fragments......Page 283
Fragments for Combinatorial Library Design......Page 284
Ligand-related combinatorial problems......Page 285
De Novo Design via Docking......Page 286
Divide-and-conquer methods......Page 287
Optimization and evolutionary methods......Page 288
Fragment-Based Design......Page 289
Physical Chemistry Methods......Page 290
Presentation, Storage, and Search......Page 291
Ensembles......Page 292
Dynamic......Page 293
Hydration......Page 294
Future Perspectives......Page 295
References......Page 296
Introduction......Page 301
Design Considerations for Libraries of all Sizes and Types......Page 303
Descriptor and property-based diversity methods, principal component analysis, clustering, and design of experiment......Page 304
Cell-based methods for diverse and focused libraries......Page 305
Privileged substructures and receptor target family design......Page 308
Pharmacophore and atom pair-based methods......Page 309
Evolutionary programming in library design......Page 312
Structure-Based Methods for Combinatorial Libraries......Page 314
Docking methods and virtual screening......Page 315
Applications of Library Design to Focused Parallel Synthesis......Page 317
Distance and connectivity......Page 318
Descriptors......Page 319
Pharmacophore models......Page 320
Combined approaches......Page 321
Homology Models......Page 322
Genetic algorithms......Page 323
References......Page 324
Introduction......Page 331
Library Design Process......Page 333
Overview......Page 334
Reagent-Based Methods......Page 336
Reagent-Biased Product-Based Methods......Page 341
Noncombinatorial Library Design......Page 348
Reagent-Based versus Product-Based Design......Page 349
Multiobjective Optimization......Page 350
Utilizing Protein Structure......Page 352
Descriptors......Page 354
Diversity and Focus......Page 357
Applications of Library Design in Lead Generation......Page 358
Building a General Screening Collection......Page 359
System-Biased Libraries......Page 360
G protein-coupled receptor-biased libraries......Page 361
Kinase-biased libraries......Page 362
Activity-Guided Design......Page 364
References......Page 366
4.16 Quantum Mechanical Calculations in Medicinal Chemistry: Relevant Method or a Quantum Leap Too Far?......Page 373
Introduction......Page 374
Theoretical Background for Quantum Mechanical Calculations......Page 376
Ab Initio Methods......Page 377
Semiempirical Methods......Page 378
Calculation of Solvent Effects Based on and in Combination with Quantum Mechanical Calculations......Page 379
Application of Quantum Mechanical Calculations to Generate Accurate Molecular Structures......Page 380
Accurate prediction of atom hybridization with quantum mechanical methods......Page 381
Quantitative structure-activity relationship using molecular orbitals from quantum mechanical calculations......Page 384
Calculation of relative energies of conformations of molecules......Page 385
Application of Quantum Mechanical Descriptors for the Prediction of Molecular Properties and use in Quantitative Structure-Activity and Structure Property Relationships......Page 387
Quantum mechanics descriptors in quantitative structure-activity relationship for molecular properties and protein-ligand binding......Page 388
Quantum mechanical descriptors in quantitative structure-activity relationship for the prediction of pKa......Page 389
Quantum mechanical calculations in quantitative structure-activity relationship for the prediction of toxicity......Page 390
New developments for quantum chemical descriptors......Page 391
Application to absorption, blood-brain barrier penetration, metabolism, bioavailability, and druglikeness......Page 392
Applications of Quantum Mechanical Calculations to Reactivity and Enzyme Mechanisms......Page 393
Applications to beta-lactamase, elastase, and hydrolysis reactions......Page 394
Applications to Cdc-42-catalyzed GTP hydrolysis, uracil-DNA glycosylase, aldose reductase, glutathione S-transferases, neuraminidases, and platelet-derived growth factor receptor (PDGFR)......Page 398
Application of quantum mechanical calculations to the stereoisomerism of a nicotinic acetylcholine receptor ligand......Page 399
Application to the discovery of new antimalarial compounds......Page 400
Application to a binding model for alpha1-adrenoceptors......Page 401
Applications to protein kinases......Page 402
Applications to cytochrome P450 and drug metabolism......Page 403
Applications to DNA interactions......Page 404
Applications to p-hydroxybenzoate hydroxylase, haloalkane dehalogenase, and thymidine kinase......Page 405
Application to thrombin......Page 406
New developments in the application of quantum mechanics to protein-ligand binding prediction......Page 407
Outlook......Page 409
References......Page 410
Pharmacological Target Space......Page 415
Chemical Properties of Drugs and Leads......Page 416
Trends in Molecular Properties......Page 417
Molecular Recognition Basis for Druggability......Page 423
Analysis of Protein Structures as Drug Targets......Page 424
References......Page 425
Introduction......Page 428
The Origin of Drug-Likeness as a Concept......Page 431
Lead-Likeness and Fragment Screening Concepts......Page 432
High-Concentration Screening using a Biochemical Assay......Page 439
The Design of Fragment Screening Sets......Page 440
Turning Fragment Hits into Leads......Page 443
Fragment Evolution......Page 444
Fragment Linking......Page 447
Fragment Optimization......Page 448
References......Page 449
Ligand-Based Virtual Screening......Page 452
Three-Dimensional Structure Generation......Page 453
Two-dimensional to three-dimensional (2D–3D) structure conversion......Page 455
Explicit conformer generation......Page 457
Structure quality measures......Page 458
Topological substructure searching......Page 459
Fingerprint and Dataprint-Based Descriptors......Page 460
Topological fingerprints/dataprints......Page 462
Geometric fingerprints/dataprints......Page 463
Reduced graph representations of structure......Page 464
Virtual Screening by Explicit Molecular Alignment......Page 465
Gaussian function evaluations......Page 466
Shape-based alignment......Page 467
Descriptor weighting paradigms......Page 468
Insights into Descriptor Selection......Page 469
Structure-Based Virtual Screening......Page 471
Clique detection......Page 472
Ligand flexibility......Page 473
Force field scoring......Page 475
Regression-based scoring......Page 476
Knowledge-Based Scoring......Page 477
Consensus Scoring, Scoring Function Comparisons, and Other Tricks and Traps......Page 479
Structure-Based Virtual Screening (SVS) Successes......Page 480
References......Page 482
Introduction......Page 488
Initial Considerations for Subset Composition and Use......Page 489
Methods for Subset Selection......Page 494
Subset Screening Analysis, Iterative Follow-Up, and Triage......Page 499
References......Page 505
Manual Design of Novel Compounds Using a Pharmacophore Hypothesis......Page 507
Discovery of Novel Compounds Using a Pharmacophore Hypothesis and Three-Dimensional Database Searching......Page 510
References......Page 527
4.22 Topological Quantitative Structure-Activity Relationship Applications: Structure Information Representation in Drug Discovery......Page 529
The Structure-Activity Relationship......Page 530
Molecular Structure as an Information Network......Page 532
Encoding Electron Counts in a Molecule......Page 533
Valence State Electronegativity......Page 534
Second hypothesis......Page 536
Third hypothesis......Page 538
Parameterization of Onium Groups......Page 540
Special Character of E-State......Page 541
The E-State Spectrum Significance......Page 542
The E-State and How it Encodes Structure Information......Page 543
The Atom-Type E-State Index......Page 544
The Group Type E-State Index......Page 545
Bond E-state formalism......Page 546
Molecular Polarity Index......Page 547
Molecular Connectivity......Page 548
Atom-type E-state descriptors as a basis for molecular structure space......Page 550
Molecular connectivity indices as a basis for molecular structure space......Page 552
The quantitation of molecular fragments, groups, and pharmacophores......Page 553
Atom-type E-state descriptors in a library similarity search......Page 554
Use of Structure Information Representation in Quantitative Structure-Activity Relationship Model Development and Interpretation......Page 556
Use of E-state in a topological superposition model for hERG inhibitors......Page 557
Summary of E-state quantitative structure-activity relationship (QSAR) studies......Page 560
References......Page 564
4.23 Three-Dimensional Quantitative Structure-Activity Relationship: The State of the Art......Page 567
Birth of Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR)......Page 568
Comparative molecular field analysis......Page 569
Self-organizing molecular field analysis (SOMFA)......Page 570
Pseudoreceptors......Page 571
Genetically evolved receptor models (GERM)/comparative receptor surface analysis (CoRSA)......Page 572
Methods of Alignment......Page 573
Pharmacophores......Page 574
Alignment-Independent Methods......Page 575
Weighted holistic invariant molecular (WHIM) descriptors......Page 576
Hologram quantitative structure-activity relationship (HQSAR)......Page 577
Neural Nets......Page 578
Validation......Page 579
Guidelines......Page 580
The Compounds......Page 581
References......Page 582
4.24 Structure-Based Drug Design - The Use of Protein Structure in Drug Discovery......Page 588
Introduction......Page 589
Design of Human Immunodeficiency Virus Protease Inhibitors against Acquired Immune Deficiency Syndrome (AIDS)......Page 590
Crystal Structure of Human Immunodeficiency Virus Protease with a Substrate-Based Inhibitor......Page 591
Structure-Based Design Leading to Ritonavir and Lopinavir: Exploiting the Symmetry of the Symmetry of the Human Immunodeficiency Virus Protease......Page 593
Structure-Based Drug Design Leading to Indinavir: Creating a Bioavailable Drug......Page 595
Structure-Based Design Leading to a Novel Nonpeptidic Scaffold......Page 598
Structure-Based Design Leading to a Substrate-Based Inhibitor with a Central Diol as Transition-State Analog......Page 599
Structure-Based Drug Design Leading to Amprenavir: Nonpeptidic Scaffold with a Novel Binding Mode to the Catalytic Aspartates......Page 601
Structure-Based Drug Design Leading to Nelfinavir......Page 603
First Crystal Structure of Neuraminidase......Page 605
Crystal Structure of Sialic Acid Complexed to Neuraminidase......Page 606
Structure-Based Design Leading to Zanamivir: Exploring the Binding Pocket of the Transition State-Like Analog......Page 608
Structure-Based Drug Design Leading to Oseltamivir: Identification of a New Apolar Pocket......Page 610
Structure-Based Drug Design Leading to Novel Inhibitors with a Cyclopentane Scaffold: Combining Combinatorial Chemistry and Structure-Based Drug Design......Page 611
Structure-Based Drug Design Investigating Differences in the Active Site: Exploring Resistant Strains......Page 614
Design of Inhibitors Binding to SH2 Domains......Page 615
First Crystal Structure of SH2 Domain......Page 616
Structure-Based Design Leading to Nonpeptidic Inhibitors......Page 617
Structure-Based Design Resulting in High-Affinity Inhibitors: Exploration of Different Scaffold Binding Modes......Page 618
Structure-Based Design Using a Fragment Approach for Replacing Phosphotyrosine......Page 622
Design of PTP-1B Inhibitors Against Diabetes......Page 627
Crystal Structure of PTP-1B with a Peptide......Page 628
Crystal Structure of PTP-1B with a Cognate Peptide......Page 629
Identification of a Second Aryl Phosphate-Binding Site by Soaking Studies......Page 630
Structure-Based Design Leading to Novel Bioavailable and Nonpeptidic Inhibitors Based on Screening Hit......Page 631
Structure-Based Drug Design Using Fragment Screening and X-ray Structure-Based Assembly......Page 634
Structure Elucidation of TCPTP: Towards Selective PTP-1b Inhibitors......Page 635
References......Page 636
Biomolecular Simulation......Page 642
Equations of Motion......Page 643
Calculation of Free Energy......Page 644
Thermodynamic Cycles......Page 645
Slow Growth......Page 647
lambda-Dynamics......Page 648
Other Methods......Page 649
Example: The Estrogen Receptor......Page 650
Simulation of Known Ligands......Page 651
Calculation of Binding Free Energies......Page 652
Structural Interpretation......Page 653
Drug Design......Page 654
Conclusions......Page 655
References......Page 656
Seven Transmembrane G Protein-Coupled Receptors: Insights for Drug Design from Structure and Modeling......Page 660
Classification of G Protein-Coupled Receptors......Page 661
Early Modeling Attempts......Page 663
The Problems Associated with De Novo Integral Membrane Protein Structure Prediction......Page 664
Nuclear Magnetic Resonance (NMR) Studies......Page 666
Site-Directed Mutagenesis and Ligand Structure-Activity Relationship......Page 667
Substituted Cysteine Accessibility Method......Page 668
Spin Labeling, Fluorescence, and Photoaffinity Mutations......Page 669
Loop Modeling of G Protein-Coupled Receptors......Page 671
Automated Modeling......Page 673
Family B Modeling......Page 674
Family C Modeling......Page 675
Structure-Based Drug Design......Page 676
Virtual Screening Using G Protein-Coupled Receptor Models......Page 678
Privileged structure approach......Page 681
Pharmacophoric fingerprint techniques......Page 682
Burden, CAS, and University of Texas (BCUT) topological molecular descriptors......Page 683
Beta-turn peptidomimetics......Page 684
Summary of G Protein-Coupled Receptor Library Design Methods......Page 685
References......Page 686
Introduction: Ion Channels......Page 693
K+ Channels as Targets for Drug Design......Page 694
KcsA: A pH-Dependent K+ Channel......Page 695
MthK: A Ca2+-Activated K+ Channel......Page 697
Kv1.2: A Mammalian Voltage-Dependent K+ Channel......Page 698
The Kir3.1 (mGIRK) Intracellular Domain......Page 699
Molecular Dynamics Simulation......Page 700
KcsA: Permeation, Gating, and Lipid Interactions......Page 701
Modeling Studies of Inwardly Rectifying K+ Channels......Page 702
Modeling Studies of Voltage-Dependent (Kv) Channels......Page 705
K+ Channel-Blocker Interactions......Page 707
K+ Channel-Drug Interactions......Page 709
References......Page 710
The Nuclear Receptor Superfamily......Page 715
Constitutive receptors......Page 716
The Ligand-Binding Mechanisms......Page 720
Ligand Adaptability......Page 721
Structure-Based Drug Design for Nuclear Receptors......Page 722
Retinoic Acid Receptors and Retinoid X Receptors......Page 723
Vitamin D Receptor......Page 724
Estrogen Receptors......Page 725
Peroxisome Proliferator-Activated Receptors......Page 727
Liver X Receptors......Page 728
Homology Modeling......Page 729
Molecular Dynamics......Page 730
Pharmacophore models......Page 731
Virtual screening......Page 732
References......Page 733
Introduction......Page 738
Protein Tyrosine Phosphatases......Page 741
Pyruvate Dehydrogenase Kinase......Page 744
Phosphodiesterases Inhibitors......Page 745
Substrate Recognition and Selectivity among Phosphodiesterase Family Members......Page 747
Phosphodiesterase-Binding Site Promiscuity......Page 749
From Clone to Crystal......Page 750
Useful Crystallographic Terminology when Utilizing Crystal Structures......Page 752
References......Page 753
Scalar Objective......Page 756
Applications in Cheminformatics......Page 757
Selecting quantitative structure-activity relationship models......Page 758
Limitations of Numerical Multiobjective Algorithms......Page 759
Conclusions......Page 760
References......Page 761
Introduction......Page 764
Introduction to human serum albumin......Page 765
Quantitative structure-activity relationship (QSAR) approaches to predicting plasma protein binding......Page 766
Structure-based approaches to predicting plasma protein binding......Page 767
Structure of cytochromes P450......Page 770
Quantitative structure-activity relationship approaches to predicting P450 inhibitors or substrates......Page 772
Structure-based approaches to predicting P450 inhibitors or substrates......Page 773
Introduction to Structure-Based Fragment Screening......Page 775
Fragment Screening......Page 777
Fragment screening with x-ray crystallography......Page 778
Virtual screening......Page 779
Libraries......Page 780
General-purpose libraries......Page 781
Version 2.0 and the rule of three......Page 782
Informatics Requirements for Fragment Screening......Page 784
AstexViewer - structure visualization......Page 785
Project pages......Page 786
Examples of Fragment Screening......Page 787
Application 2: thrombin......Page 788
References......Page 791
Chemical and Biological Similarity......Page 796
Example......Page 797
Searching for Selectivity or Differentiation......Page 798
Structural Clustering......Page 800
Clustering of Sequences......Page 802
Chemogenomics......Page 804
References......Page 805