Pharmaciphore Perception, Development, and Use in Drug Design


IUL Biotechnology Series, 2
by Osman F. Guner (Editor)
Edition: First

Book Details:

  • Series: IUL Biotechnology Series
  • Volume: 2
  • Binding: Hardcover 
  • Pages: 560
  • Dimensions (in inches): 1.75 x 9.75 x 6.50
  • Publisher: International University Line 
  • Publication Date: March 9, 2000
  • ISBN: 0-9636817-6-1
  • Price: $109.95

Look Inside This Book


edited by

Preface     xv
List of Contributors     xxi
1.    Evolution of the Pharmacophore Concept in Pharmaceutical Research     3
       Peter Gund
2.    Manual Pharmacophore Generation: Visual Pattern Recognition     17

Osman F. Güner
3.    Pharmacophore Definition Using the Active Analog Approach     21
       Denise D. Beusen and Garland R. Marshall
       3.1.    Introduction     23
       3.2.    The Active Analog Approach     24
       3.3.    Systematic Search and the Rigid Geometry Approximation     26
       3.4.    Combinatorial Nature of Systematic Search     26
       3.5.    Strategies for Defeating the Combinatorial Explosion     27
                 3.5.1.    Rigid Body Rotations and Building Molecules from Aggregates     27
                 3.5.2.    Look-Ahead     31
                 3.5.3.    Energy Filtering     31
                 3.5.4.    Ping Closures     31
       3.6.    Systematic Search Parameters Which Impact Sampling Completeness     33
       3.7.    Analysis of Datasets     38
       3.8.    Considerations in Using the Active Analog Approach     39
                 3.8.1.    Pharmacophore vs. Active-Site Models     39
                 3.8.2.    Selection of Molecules for Analysis     40
                 3.8.3.    Interpreting the Results     40
       3.9.    Examples of the Application of the Active Analog  Approach     41
                 3.9.1.    Morphiceptin Analogs     41
                 3.9.2.    ACE Inhibitors     42
                 3.9.3.    Substance P Antagonists     43
Automated Pharmacophore Development Systems     47
4.    DISCO: What We Did Right and What We Missed      49
       Yvonne Connolly Martin
       4.1.    Overview of DISCO     51
       4.2.    The Problem of Searching for Correspondences     54
       4.3.    The Importance of Conformational Searching     55
       4.4.    Selecting the Conformational Ensemble     56
       4.5.    Selecting Compounds for the DISCO Analysis     58
       4.6.    What DISCO Uses as Points to Match     60
       4.7.    Tolerance: The Trade-Off Between a Close Match and Including More Points     61
       4.8.    Typical Outcomes and Strategies for Follow-Up     62
       4.9.    Shortcomings in DISCO     63
       4.10.  DISCO in the Age of HTS and Molecular Diversity     64
5.    HipHop: Pharmacophores Based on Multiple Common-Feature Alignments     69
       Omoshile O. Clement and Adrea Trope Mehl
       5.1.    Background     71
       5.2.    Methodology     72
                 5.2.1.    General Considerations     72
                 5.2.2.    Algorithm     73
                 5.2.3.    Choosing Relevant Conformations     73
                 5.2.4.    Feature Definition     74
                 5.2.5.    Generating Common Feature Hypotheses Using HipHop     74
                 5.2.6.    Input Conformers     75
                 5.2.7.    Parameter Setting     75
       5.3.    Applications     77
                 5.3.1.    5HTP-3 Antagonists     77
                 5.3.2.    ETA Endothelin Antagonists     79
                 5.3.3.    HIV-1 Protease Inhibitors     79
       5.4.    Conclusion     82
6.    GASP: Genetic Algorithm Superimposition Program     85
       Gareth Jones and Peter Willett
        6.1.    Introduction     87
        6.2.    The Chromosome Representation and the Genetic Operators     89
                  6.2.1.    Input of 3D Structures     89
                  6.2.2.    Chromosome Representation     90
                  6.2.3.    Genetic Operators     91
        6.3.    The Fitness Function     92
                  6.3.1.    Generation of Conformations and Least-Squares Fitting     93
                  6.3.2.    Calculation of the van der Waals Energy     94
                  6.3.3.    Calculation of the Volume Integral     94
                  6.3.4.    Calculation of the Similarity Score     95
       6.4.     Identification of Pharmacophores Using GASP     101
                  6.4.1.    Leu-Enkephalin and a Hybrid Morphine     101
                  6.4.2.    Overlay of Four 5-HT3 Antagonists     102
                  6.4.3.    Overlay of Six Angiotensin II Receptor Antagonists     103
       6.5.     Conclusion     104
7.    Exploring Pharmacophores with Chem-X     107
       Stephen J. Cato
       7.1.    Introduction     109
       7.2.    Centers in Chem-X     110
       7.3.    Centers in 3D Searching     111
       7.4.    Pharmacophore Keys Defined     114
       7.5.    Generating Pharmacophore Keys     117
       7.6.    Working with Pharmacophore Keys     119
       7.7.    Pharmacophore Diversity     121
       7.8.    Virtual Screening     123
       7.9.    Diamond Pharmacophores     124
Predictive Model Development—3D QSAR     127
8.    Apex-3D: Activity Prediction Expert System with 3D QSAR     129
Erich R. Vorpagel and Valery E. Golender
       8.1.    Introduction     131
       8.2.    General Description of Apex-3D     133
       8.3.    Steroid Binding Data     136
       8.4.    Conformer Generation Strategy     137
       8.5.    Activity Classification Analysis     138
                 8.5.1.    Pharmacophores Identified     140
                 8.5.2.    Prediction (Classification) Results     141
       8.6.    3D QSAR Models     141
                 8.6.1.    Testosterone-Binding 3D QSAR Model     142
                 8.6.2.    Corticosteroid 3D QSAR Model     144
       8.7.    Comparison with other 3D QSAR Methods     145
       8.8.    Conclusions     147
9.    Pharmacophore Models and Comparative Molecular Field Analysis (CoMFA)     151
       Robert D. Clark, Joseph M. Leonard, and Alexander Strizhev
       9.1.    What Is CoMFA?     153
       9.2.    Alignment Rules     155
       9.3.    The s Receptor Dataset     156
       9.4.    Charges and Energy Minimization     159
       9.5.    Identifying an Initial Query     160
       9.6.    Refining the Query     162
       9.7.    Evaluating the Model     163
       9.8.    Applicability     166
10.  HypoGen: An Automated System for Generating 3D Predictive Pharmacophore Models     171
       Hong Li, Jon Sutter, and Rémy Hoffmann
       10.1.    Introduction     173
       10.2.    General Strategy     174
       10.3.    Methodology     175
                   10.3.1.    Preparing to Run HypoGen     176
                   10.3.2.    Running HypoGen     179
      10.4.    Case Study     179
      10.5.    Conclusion     187
Applications in Drug Design     191
11.  Metric for Analyzing Hit Lists and Pharmacophores      193

       Osman F. Güner and Douglas R. Henry
       11.1.    Introduction     195
       11.2.    Results and Discussion     196
                  11.2.1.    Application Examples    205
       11.3.    Conclusions     210
12.  Strategies for Database Mining and Pharmacophore Development     213
       Osman F. Güner, Marvin Waldman, Rémy Hoffmann, and Jong-Hoon Kim
       12.1.    Introduction     215
       12.2.    Methods     217
       12.3.    Results and Discussion     217
                   12.3.1.    Use of Query Clustering and Merging     217
                   12.3.2.    Receptor-Based versus Ligand-Based Pharmacophore Models     221
                   12.3.3.    Use of Shape versus Pharmacophore versus Merged Pharmacophore/Shape Queries     223
                   12.3.4.    The Significance of Training Set Selection: Using Similar versus Diverse Compounds     227
                   12.3.5.    Manual versus Automated Pharmacophore Model Generation     230
                   12.3.6.    Rigid versus Flexible Searching     231
13.  Pharmacophore Modeling by Automated Methods: Possibilities and Limitations     237
       Morten Langgård, Berith Bjørnholm, and Klaus Gundertofte
       13.1.    Introduction     239
                   13.1.1.    Pharmacophore Models     240
       13.2.    The Methods     241
                   13.2.1.    Flo96     241
                    13.2.2.    Catalyst     243
      13.3.    Results and Discussion     244
                  13.3.1.    Flo96    244
                  13.3.2.    Catalyst     246
      13.4.    Conclusion     249
14.  Database Mining Using Pharmacophore Models to Discover Novel Structural Prototypes     251
James J. Kaminski, Dinanath. F. Rane, and Marnie L. Rothofsky
15.  Predicting Drug-Drug Interactions in Silico Using Pharmacophores:  Paradigm for the Next Millennium     269
       Sean Ekins, Barbara J. Ring, Gianpaolo Bravi, James H. Wikel, and Steven A. Wrighton
       15.1.    Introduction     271
       15.2.    Shifting the Drug Metabolism Paradigm     273
       15.3.    Cytochrome P450 Pharmacophore Modeling Methodology     278
       15.4.    Utilizing and Interpreting Data Generated by Computational Models for Enzymes and
                  Transporters Involved in Drug Metabolism     282
       15.5.    The Challenge Ahead     286
       15.6.    Concluding Remarks     288
16.  Feature-Based Pharmacophores: Application to Some Biological Systems     301

       Rémy. Hoffmann, Hong Li, and Thierry Langer
      16.1.    Introduction     303
      16.2.    First Case: TXSI-TXRA     304
                  16.2.1.    Introduction     304
                  16.2.2.    Methods     305
                  16.2.3.    Results and Discussion     306
       16.3.   Second Case: Retinoic Acid Receptor Ligands     307
                  16.3.1.    Introduction     307
                  16.3.2.    Training Set and Methods     308
                  16.3.3.    Results and Discussion     310
      16.4.    Third Case: Feature-Based Pharmacophores Derived from Structural Information     312
                  16.4.1.    Introduction     312
                  16.4.2.    Methods     313
                  16.4.3.    Results and Discussion     315
                  16.4.4.    Mapping the Chemical Features on the Active Conformation of Methotrexate     316
       16.5.    Conclusion     316
17.  Receptor-Based Pharmacophore Perception and Modeling     339
       C. M. Venkatachalam, Paul Kirchhoff, and Marvin Waldman
      17.1.    Introduction     321
      17.2.    Pharmacophore Definition     322
      17.3.    Retinoid Series 1     327
      17.4.    Retinoid Series 2     328
      17.5.    Retinoid Series 3     329
      17.6.    Pharmacophore Validation     331
      17.7.    Conclusions     333
18.  Receptor-Based Pharmacophore Perception and Modeling     339
       C. M. Venkatachalam, Paul Kirchhoff, and Marvin Waldman
      18.1.    Introduction     341
      18.2.    Cerius2 Structure-Based Focusing Method     342
                  18.2.1.    Active Site     343
                  18.2.2.    Ludi Interaction Map     344
                  18.2.3.    Cluster Analysis of the Interaction Map     344
                  18.2.4.    Multiples Queries     345
                  18.2.5.    Volume Exclusions     345
                  18.2.6.    Catalyst Database Search     346
       18.3.    Results and Discussion for the Estrogen Binding     346
19.   Pharmacophore-Based Molecular Docking     351
        Bert E. Thomas IV, Diane Joseph-McCarthy, and Juan C. Alvarez
       19.1.    Introduction     353
       19.2.    The Dock Algorithm     355
                   19.2.1.    Conformational Ensemble Docking     356
       19.3.    Pharmacophore-Based Docking     357
                  19.3.1.    Database Preparation     360
                  19.3.2.    Site Point Generation     361
                  19.3.3.    Validation     361
       19.4.    Discussion    362
Applications in Drug Design     369
20.  The Use of Multiple Excluded Volumes Derived from X-Ray Crystallographic Structures in 3D Database
       Searching and 3D QSAR     371

       Mikael Gillner and Paulette Greenidge
       20.1.    Introduction     373
       20.2.    Background     374
       20.3.    Methods     376
                  20.3.1.    Catalyst Pharmacophore Construction and 3D QSAR     376
       20.4.    Results and Discussion     378
       20.5.    Conclusion     382
21.   Docking-Derived Pharmacophores from Models of Receptor-Ligand Complexes     385
        Renate Griffith, John B. Bremner, and Burak Coban
        21.1.    Introduction     387
        21.2.    Agonist and Antagonist Binding Site(s) on the Adrenergic Receptors     389
        21.3.    3D Model Building and Docking     391
                    21.3.1.    Revision of the Models     394
                    21.3.2.    Docking of Adrenaline     395
                    21.3.3.    Docking of a Rigid Antagonist (IQC)     397
       21.4.    Construction of Docking-Derived Pharmacophores    398
                   21.4.1.    The a1A Pharmacophore     399
                   21.4.2.    The a1B pharmacophore     400
                   21.4.3.    Comparing the Docking-Derived Pharmacophores     400
                   21.4.4.    Design of Potentially a1B-Selective Ligands     401
22.  Technique for Developing a Pharmacophore Model that Accommodates Inherent
       Protein Flexibility: An Application to HIV-1 Integrase     409
       Kevin M. Masukawa, Heather A. Carlson, and J. Andrew McCammon
       22.1.    Introduction     411
       22.2.    Computational Details     413
                   22.2.1.    The MD Simulation and Preparation of the Available Crystal Structures     413
                   22.2.2.    MUSIC     414
                   22.2.3.    The Dynamic Pharmacophore Model for HIV-1 Integrase     415
                   22.2.4.    Static Pharmacophore Models     418
                   22.2.5.    Using the Catalyst Programs     418
       22.3.    Results and Discussion      420
       22.4.    Conclusion     423
23.  Pharmacophores Derived from the 3D Substructure Perception      431
       Sandra Handschuh and Johann Gasteiger
       23.1.    Introduction     433
       23.2.    General Principles of the Genetic Algorithm     434
                   23.2.1.    Encoding of the Individuals     436
                   23.2.2.    Optimization Criteria     437
                   23.2.3.    The Genetic and Nongenetic Operators—A Short Description     438
                   23.2.4.    The Pareto Fitness of Individuals     442
       23.3.    Matching the Conformations—Directed Tweak     444
       23.4.    Special Features of the Program     447
                   23.4.1.    Close Contact Check of van der Waals Radii     447
                   23.4.2.    Matching Criteria     447
                   23.4.3.    Superimposition Restrictions     449
       23.5.    Summary     450
24.  The Electron-Conformational Method of Identification of Pharmacophore and Anti Pharmacophore
       Shielding     455
       Isaac B. Bersuker, Süleyman Bahçeci, and James E. Boggs
       24.1.    Introduction: Improved Definition of Pharmacophore     457
       24.2.    Description by EC Matrices and Pharmacophore Identification     459
       24.3.    Anti-Pharmacophore Shielding and Other Auxiliary Groups. Formula of Activity     462
       24.4.    Parameterization and Results     464
                   24.4.1.    Angiotensin Converting Enzyme (ACE) Inhibitors     466
                   24.4.2.    Rice Blast Activity (RBA)     470
       24.5.    Conclusions     472
25.  Development and Optimization of Property-Based Pharmacophores     477
       Ali G. Özkabak, Mitchell A. Miller, and Douglas R. Henry
       25.1.    Introduction     479
       25.2.    Flexible Substructure- or Field-Based Superposition of Structures     482
       25.3.    Identification of Relevant Functional and Physicochemical Property Groups     484
       25.4.    Generating an Initial Pharmacophore     487
       25.5.    Optimizing the Pharmacophore     490
       25.6.    Summary     493
26.  Effect of Variable Weights and Tolerances on Predictive Model Generation     499
       Jon Sutter, Osman Güner, Rémy Hoffmann, Hong Li, and Marvin Waldman
       26.1.    Background     501
       26.2.    Methodology     502
       26.3.    Cost Function     504
       26.4.    Case Study     506
       26.5.    Conclusions     510
27.  Future Directions In Pharmacophore Discovery     515
       John H. Van Drie
       27.1.    Introduction     517
       27.2.    Goals for a Pharmacophore Discovery Method     520
                   27.2.1.    Objectivity     520
                   27.2.2.    Completeness     521
                   27.2.3.    Robustness     522
                   27.2.4.    Computational Controls     523
                   27.2.5.    Statistical Measures of Quality     524
                   27.2.6.    Prospective Applications of Pharmacophores     524
       27.3.    Not All Datasets Are Created Equal     525
       27.4.    Conformational Analysis     526
       27.5.    Frontiers     527
Index     531

Perceiving a pharmacophore is the most important first step towards understanding the interaction between a receptor and a ligand. In the early 1900s, Paul Ehrlich offered the first definition for a pharmacophore: “a molecular framework that carries (phoros) the essential features responsible for a drug’s (pharmacon) biological activity” (Ehrlich P: Dtsch Chem Ges 1909, 42:17). That definition of a pharmacophore remained unperturbed for over 90 years. The current widely used definition was presented by Peter Gund in 1977: “a set of structural features in a molecule that is recognized at a receptor site and is responsible for that molecule’s biological activity”  (Gund P: Prog Mol Subcell Biol 1977, 5:117-143). This modern definition is remarkably loyal to the earliest definitions.
      It is only appropriate, then, that this books starts with the “Evolution of the Pharmacophore Concept in Pharmaceutical Research” in Chapter 1, written by Peter Gund who also developed the first 3D searching software, Molpad, and the first ideas for computational pharmacophores.2 It took 15 years for the commercial 3D searching software to become available following its original publication by Gund, Langridge, and  Wipke
(Gund P, Wipke WT, Langridge R: Proc Intl Conf on Computers in Chem Res and Educa, Ljubljana, 1973:5/33). John Van Drie, on the other hand, was involved with the development of two commercial 3D searching software applications: one of the earliest ones, Aladdin, and the latest one, Catalyst®. He has also been active and forward-looking in his work with pharmacophores as, for example, “shrink-wrap pharmacophores,” (Van Drie J: J Chem Inf Comput Sci 1997, 37:38-42) and in his program Dante (Van Drie J: J Comput-Aided Mol Des 1997, 11:39-52). It is only appropriate, then, to end this book with Chapter 27 on the “Future Directions in Pharmacophores Discovery,” written by John Van Drie.
     The information provided in between these two chapters is pretty much everything you would want to know about pharmacophores, and probably more.
      The Part II of the book is dedicated to analog-based pharmacophores. Your editor, Osman Güner, presents an elementary introduction to the concept of pharmacophores in Chapter 2, entitled “Manual Pharmaco-phore Generation: Visual Pattern Recognition.” The analog-based pharmacophore development is then introduced with the description of the Active Analog Approach by Denise Beusen and Garland Marshall in Chapter 3: “Pharmacophore Definition Using the Active Analog Approach.”
      This information is followed by a detailed description in Chapters 4 through 10 of various pharmacophore development and 3D-QSAR software by the pioneers in this area, their inventors, and current developers. Yvonne Martin describes and critically evaluates one of the earlier pharmacophore development software products, DISCO, in Chapter 4 entitled “DISCO: What We Did Right and What We Missed.” Chapter 5 describes a common-feature based alignment software, “HipHop: Phar-macophores Based on Multiple Common-Feature Alignments,” written by Omoshile Clement and Adrea Mehl. Gareth Jones, Peter Willett, and Robert Glen describe one of the recent alignment and pharmacophore software in Chapter 6: “GASP: Genetic Algorithm Superimposition Program.” Stephen Cato then describes how to perceive pharmacophores in Chapter 7 with “Exploring Pharmacophores with Chem-X.” Moving onto the predictive model generation software, Erich Vorpagel and Valery Golander describe the importance of negative activities in Chapter 8  entitled “Apex-3D: Activity Prediction Expert System with 3D-QSAR.” Robert Clark, Joseph Leonard, and Alexander Strizhev emphasize the importance of molecular alignment in Chapter 9: “Pharmacophore Models and Comparative Molecular Field Analysis (CoMFA).” Finally, Hong Li, Jon Sutter, and Rémy Hoffmann bring the description of the methodology under Catalyst/HypoGen in Chapter 10 entitled “HypoGen: An Automated System for Generating 3D Predictive Pharmacophore Models.”
       Chapters 11 through 17 involve various applications of analog-based pharmacophores and success stories. Chapter 11 introduces some scoring techniques for pharmacophores and hit lists including “Metric for Ana-lyzing Hit Lists and Pharmacophores,” written by Osman Güner and Douglas Henry. Different database querying strategies are introduced by Osman Güner, Marvin Waldman, Rémy Hoffmann, and Jong-Hoon Kim in Chapter 12 entitled “Strategies in Database Mining and Pharmacophore Development.” Evaluation of automated methods is presented in Chapter 13, “Pharmacophore Modeling by Automated Methods: Possibil-ities and Limitations,” by Morten Langgård, Berith Bjørnholm, and Klaus Gundertofte. A successful example of the identification of novel structures is provided by James Kaminski, Dinanath Rane, and Marnie Rothofsky in Chapter 14 entitled “Database Mining Using Pharmaco-phore Models to Discover Novel Structural Prototypes.” Other successes with pharmacophores are described in Chapters 15 through 17 with “Predicting Drug-Drug Interactions in Silico using Pharmacophores: A Paradigm for the Next Millennium” by Sean Ekins, Barbara Ring, Gianpaolo Bravi, James Wikel, and Steven Wrighton. “Feature-Based Pharmacophores: Applications to Some Biological Systems” by Rémy Hoffmann, Hong Li, and Thierry Langer; and “Pharmacophore Defini-tion of Retinoid-X-Receptor Specific Ligands” by Steven K. White.
       Part III deals with more recent ideas on receptor-based pharmacophores. It starts with “Receptor-Based Pharmacophore Perception and Modeling” in Chapter 18 by Venkatachalam, Paul Kirchhoff, and Marvin Waldman, Followed by Chapter 19 where Bert Thomas IV, Diane Joseph-McCarthy, and Juan Alvarez describe “Pharmacophore-Based Molecular Docking.” Successful applications of receptor-based pharmacophores are presented in the next two Chapters 20 and 21: “Pharmacophores Including Multiple Excluded Volumes Derived from X-Ray Crystallographic Structures of Nuclear Receptors: Their Application in 3D Database Searching and 3D-QSAR” by Mikael Gillner and Paulette Greenidge, and “Docking-Derived Pharmacophores from Models of Receptor-Ligand Complexes” by Renate Griffith, John Bremner, and Burak Coban. The complications arising from the flexibility of the receptor structure is covered by Kevin Masukawa, Heather Carlson, and Andrew McCammon in Chapter 22 entitled “Technique for Developing a Pharmacophore Model That Accommodates Inherent Protein Flexibility: An Application to HIV-1 Integrase.”
       Part IV provides new ideas and algorithms in pharmacophore development. It starts with a contribution from Sandra Handschuh and Johann Gasteiger in Chapter 23 entitled “Pharmacophores Derived from the 3D Substructure Perception.” Isaac Bersuker, Süleyman Bahçeci, and James Boggs present “The Electron-Conformational Method of Identification of Pharmacophore and Anti-Pharmacophore Shielding” a novel perspective towards pharmacophore identification. Ali Özkabak, Mitchell Miller, Douglas Henry, and Osman Güner discuss the concept of pharmacophore optimization in Chapter 25, “Development and Optimization of Property-Based Pharmacophores.” An enhancement to predictive pharmacophore model generation, “Effect of Variable Weights and Tolerances on Predictive Model Generation,” is introduced in Chapter 26 by Jon Sutter, Osman Güner, Rémy Hoffmann, Hong Li, and Marvin Waldman
       Finally, Chapter 27 provides a closing with John Van Drie’s perception of the future directions in this area.
      If you are new to this area, you should start with Chapters 1, 2, 3, and 27 for an introduction; then move on to the desired software discussions, DISCO at 4, HipHop at 5, GASP at 6, Chem-X at 7, Apex-3D at 8, CoMFA at 9, and HypoGen at 10. If you feel you still need to be persuaded that these approaches are effective, you will want to read Chapters 14, 15, 16, and 17 for some real-world success stories.
       Seasoned database searchers who want to improve their skills should first understand the limitations outlined in Chapter 26, and then enrich their portfolio of different querying techniques with Chapter 25. Finally, they can learn how to analyze their hit lists by reading Chapter 11.
      If the receptor structure is available and you want to use it to improve your pharmacophore models, different techniques in this area are described in Chapters 18 and 19 and successful applications of receptor-based pharmacophores are presented at sections 20 and 21. You should also read chapter 22 to appreciate the conformational flexibility of the receptor structure and its impact on pharmacophore models.
      If you are involved in development of software tools in this area, several new ideas and algorithms are detailed in Chapters 23, 24, and 26. If you are interested in automating the pharmacophore optimization process, Chapter 25 provides good ideas and Chapter 11 provides some scoring functions that can be used for this purpose.
    To get the full picture of pharmacophores, it is always good to go back and read Chapters 1 and 27 for a historical perspective and future directions.
       In closing, consider the history of aviation with the very first flight taking place in the early 1900s and, the moon landing a mere 50 years later. Contrast this to the first use of the term “pharmacophore.” It was first used in early 1900s as Peter Gund explains in Chapter 1; however, the meaning of the definition remained remarkably unperturbed during its close to 90 years of existence. Today, pharmacophores are considered one of the most important types of “information” that can be obtained from receptor-ligand interactions. Yet, quite surprisingly, this book is the first book that has the word “pharmacophore” in its title. We therefore wanted to be very comprehensive in this first volume, covering all aspects of pharmacophore perception, development, and use in drug design. We hope that you will find this book useful to bring your computer-aided drug design endeavor to a higher level.

       Happy discoveries…

       Osman F. Güner


Pharmaciphore Perception, Development, and Use in Drug Design