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3D-QSAR and physical property modeling using quantum mechanically derived molecular surface properties [Elektronische Ressource] / vorgelegt von Kendall Grant Byler

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3D-QSAR and Physical Property Modeling Using Quantum-Mechanically-Derived Molecular Surface Properties A Dissertation Kendall Byler 2007 3D-QSAR and Physical Property Modeling Using Quantum Mechanically Derived Molecular Surface Properties Den Naturwissenschaftlichen Fakultäten der Friedrich-Alexander-Universität Erlangen-Nürnberg zur Erlangung des Doktorgrades vorgelegt von Kendall Grant Byler aus Huntsville Als Dissertation genehmigt von den naturwissenschaftlichen Fakultäten der Friedrich-Alexander-Universität Erlangen-Nürnberg. Tag der mündlichen Prüfung: 11.05.2007 Vorsitzender der Promotionskomission: Prof. Dr. E. Bänsch Erstberichterstatter: Prof. Dr. T. Clark Zweitberichterstatter: Prof. Dr. P. Gmeiner Acknowledgements I would like to thank those but for whom this work would not have been possible. The first of these is Professor Dr. Tim Clark, who provided the opportunity and the guidance in my study of computational chemistry. And thanks go to the members of the Clark group who helped me in my endeavors: Dr. Nico van Eikema Hommes, Dr. Harald Lanig, Dr. Ralph Puchta, Dr. Matthias Hennemann, Matthias Brüstle, Anselm Horn, Dr. Olaf Othersen, Dr. Gudrun Schürer, Dr. Tatyana Shubina, Florian Haberl, Kirsten Höhfeld, Catalin Rusu, Jr-Hung Lin, Hakan Kayi, and Sergio Sanchez.
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3D-QSAR and Physical Property
Modeling Using Quantum-Mechanically-
Derived Molecular Surface Properties
A Dissertation

Kendall Byler
2007 3D-QSAR and Physical Property Modeling Using
Quantum Mechanically Derived Molecular Surface
Properties





Den Naturwissenschaftlichen Fakultäten der
Friedrich-Alexander-Universität Erlangen-Nürnberg
zur
Erlangung des Doktorgrades


vorgelegt von
Kendall Grant Byler
aus Huntsville

Als Dissertation genehmigt von den naturwissenschaftlichen
Fakultäten der Friedrich-Alexander-Universität Erlangen-Nürnberg.






















Tag der mündlichen Prüfung: 11.05.2007
Vorsitzender der Promotionskomission: Prof. Dr. E. Bänsch
Erstberichterstatter: Prof. Dr. T. Clark
Zweitberichterstatter: Prof. Dr. P. Gmeiner
Acknowledgements














I would like to thank those but for whom this work would not have been
possible. The first of these is Professor Dr. Tim Clark, who provided the opportunity
and the guidance in my study of computational chemistry. And thanks go to the
members of the Clark group who helped me in my endeavors: Dr. Nico van Eikema
Hommes, Dr. Harald Lanig, Dr. Ralph Puchta, Dr. Matthias Hennemann, Matthias
Brüstle, Anselm Horn, Dr. Olaf Othersen, Dr. Gudrun Schürer, Dr. Tatyana
Shubina, Florian Haberl, Kirsten Höhfeld, Catalin Rusu, Jr-Hung Lin, Hakan Kayi,
and Sergio Sanchez. And also to members of the Gasteiger group for their
assistance: Dr. Simon Spycher, Prof. Dr. Fernando da Costa, Dimitar Hristozov, Dr.
Christof Schwab, and Dr. Thomas Engel, and of course Adrian Jung of the Kirsch
group. Thanks also to the Pfizer Corporation for their financial support of this
research.
I would thank my family: my parents, Paul and Carol Byler, my sister,
Ashley, my grandparents, Henry and Martha Snoddy, Elza and Emma Byler, and
my beautiful wife, Anastasia. And I would thank the friends everywhere that stayed
friends despite the separations of time and distance.
i

Contents

1 Introduction ................................................................................................1
1.1 Drug Discovery............................................................................................1
1.2 Property Modeling ......................................................................................3
1.3 A Quantum-Mechanical, Molecular Orbital Approach..........................4
2 Surface-Integral QSPR Models: Local Energy Properties ....................7
2.1 Introduction.................................................................................................7
2.1.1 Local Molecular Properties...............................................................8
2.1.2 Surface-Integral Models....................................................................9
2.2 Methods......................................................................................................15
2.3 Results ........................................................................................................16
2.3.1 Octanol/Water Partition Coefficient ...............................................16
2.3.2 Free Energy of Solvation ................................................................23
2.3.2.1 Free Energy of Solvation in Octanol ...................................................... 23
2.3.2.2 Free Energy of Solvation in Water ......................................................... 28
2.3.3 Acid Dissociation Constant.............................................................33
2.3.4 Boiling Point ...................................................................................36
2.3.5 Glass Transition Temperature40
2.3.6 Aqueous Solubility..........................................................................44
2.4 Discussion...................................................................................................48
2.5 Conclusions................................................................................................51
ii
3 Support Vector Classification of Phospholipidosis-Inducing Drugs... 52
3.1 Introduction...............................................................................................52
3.1.1 Phospholipidosis .............................................................................52
3.1.2 Phospholipidosis Models ................................................................54
3.1.3 Surface Autocorrelations56
3.1.4 Statistical Methods..........................................................................57
3.1.4.1 Support Vector Machines......................................................................57
3.1.4.2 Multivariate Adaptive Regression Splines............................................60
3.2 Methods......................................................................................................61
3.3 Results........................................................................................................62
3.3.1 Support Vector Machines ...............................................................63
3.3.2 Multivariate Adaptive Regression Splines
Using Autocorrelation Indices.......................................................68
3.4 Discussion ..................................................................................................70
3.5 Conclusions................................................................................................73
4 3D-QSAR Using Local Properties .......................................................... 74
4.1 Introduction...............................................................................................74
4.1.1 Comparative Molecular Field Analysis ..........................................74
4.1.2 Partial Least Squares Regression....................................................76
4.1.3 Local Properties ..............................................................................77
4.2 Computational Methods...........................................................................79
4.3 Results and Discussion80
4.3.1 Serotonin Receptor Agonists/Antagonists......................................80
4.3.2 Adrenergic Receptor Agonists/Antagonists....................................84
4.3.3 Dopamine D4 Antagonists..............................................................86
4.3.4 Avian Influenza Neuraminidase Inhibitors.....................................89
4.3.5 Mutagenic Tertiary Amides............................................................92
iii
4.3.6 The Effect of Grid Orientation on Predictivity ...............................96
4.4 Conclusions..............................................................................................101
5 Conclusions and Outlook.......................................................................103
5.1 Conclusions103
5.2 Outlook.....................................................................................................104
6 Summary .................................................................................................106
7 Zusammenfassung ..................................................................................110
Appendix A..................................................................................................114
Appendix B151
References....................................................................................................152

iv

Chapter 1


Introduction

1.1 Drug Discovery

1It has been estimated that, out of a pool of millions of compounds screened,
10,000 reach the animal testing phase, which will then likely produce ten drug candidates
for human clinical trials, of which only one will reach the market. It may also require 15
years and 750,000 U.S. dollars in the process. Drug candidates that fail late in the testing
process will never produce a return for the company that has invested so much time and
money. Pharmaceutical companies must offset these losses by recouping the expenditure
from among the several successfully tested drugs they produce.
In an effort to minimize the potential loss from focusing on compounds that will
never result in a marketable drug, much preliminary research and testing are done. The
2rational drug-design approach to this problem begins by identifying a molecular target
involved in a pathophysiological process and characterizing its structure and function;
then begins the search for a lead compound. This is usually achieved by means of an array
of in vitro screens for biological activity. Large groups of compounds may be evaluated
simultaneously in this way and the procedure is referred to as high-throughput screening
(HTS). Once a lead compound is discovered, it may also be found to have some
undesirable properties such as high toxicity, poor bioavailability or pharmacokinetics.
Libraries of compounds may be synthesized that have modifications to the general
structure of the lead compound in an effort to modulate the desirable and undesirable
1 Introduction
effects. Structure-activity relationships (SAR’s) may be observed concurrently with the
study of the combinatorial library that point to a common chemical substructure that
produces the pharmacological effect. The medicinal chemist can then make various
modifications to the pharmacophore in order to improve its properties.
3 Kubinyi describes the drug-design process in terms of a design cycle wherein the
4optimization of a lead compound is improved iteratively in an evolutionary manner
(Figure 1.1).


Computer-aided design:Biological
Protein crystallography, NMR,
Concept
3D databases, de novo design
Structure-activity
relationships, QSAR,
molecular modeling
Series design,
synthesis designLead Structures
Biological
Syntheses Testing
Candidates for
further developmentNew Drug Investigational New Drug

4Figure 1.1 The drug design cycle from Kubinyi’s lectures on drug design .


However, all of this takes quite a lot of time and the questions of clinical
development and lengthy drug approval process have yet to be addressed. Thus, to
improve the efficiency of the HT screen further, chemists use molecular-modeling
schemes to calculate properties based on chemical structure to aid in the screening
process. These virtual-screening methods include molecular-dynamics simulations,
protein-ligand docking, protein-protein docking, membrane simulation, similarity
searching of pharmacophore databases, and quantitative structure-activity relationships
2

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