Computational Drug Design: A Guide for Computational and Medicinal Chemists

COMPUTATIONAL DRUG DESIGN COMPUTATIONAL DRUG DESIGN A Guide for Computational and Medicinal Chemists DAVID C YOUNG Computer Sciences Corporation Copyright # 2009 by John Wiley & Sons, Inc All rights reserved Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, 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within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002 Wiley also publishes its books in a variety of electronic formats Some content that appears in print may not be available in electronic formats For more information about Wiley products, visit our web site at www.wiley.com Library of Congress Cataloging-in-Publication Data: Young, David C., 1964– Computational drug design / David C Young p.; cm Includes bibliographical references and index ISBN 978-0-470-12685-1 (cloth/CD) Drugs—Design—Mathematical models Drugs—Design—Data processing I Title [DNLM: Computational Biology—methods Drug Design Biochemical Phenomena Chemistry, Pharmaceutical—methods Drug Delivery Systems Models, Chemical QV 744 Y69c 2009] RS420 Y68 2009 6150 190285 dc22 2008041828 Printed in the United States of America 10 This book is dedicated to my grandfathers, Harvey Turner and Ray Young Harvey Turner had the intelligence to work his way up from a draftsman to Chief Engineer at Donaldsons Then he had the wisdom to leave that high pressure career behind and spend the next two decades teaching art Ray Young dropped out of high school to help make ends meet during the great depression He never returned to school, but was the most widely read and knowledgeable person I have every met CONTENTS PREFACE xv ACKNOWLEDGMENTS xix ABOUT THE AUTHOR xxi SYMBOLS USED IN THIS BOOK xxiii BOOK ABSTRACT xxix Introduction 1.1 A Difficult Problem, 1.2 An Expensive Problem, 1.3 Where Computational Techniques are Used, Bibliography, PART I THE DRUG DESIGN PROCESS Properties that Make a Molecule a Good Drug 2.1 Compound Testing, 10 2.1.1 Biochemical Assays, 11 2.1.2 Cell-Based Assays, 13 vii viii CONTENTS 2.1.3 Animal Testing, 14 2.1.4 Human Clinical Trials, 15 2.2 Molecular Structure, 16 2.2.1 Activity, 16 2.2.2 Bioavailability and Toxicity, 24 2.2.3 Drug Side Effects, 26 2.2.4 Multiple Drug Interactions, 26 2.3 Metrics for Drug-Likeness, 27 2.4 Exceptions to the Rules, 33 Bibliography, 35 Target Identification 41 3.1 Primary Sequence and Metabolic Pathway, 41 3.2 Crystallography, 43 3.3 2D NMR, 44 3.4 Homology Models, 45 3.5 Protein Folding, 45 Bibliography, 46 Target Characterization 47 4.1 Analysis of Target Mechanism, 47 4.1.1 Kinetics and Crystallography, 48 4.1.2 Automated Crevice Detection, 48 4.1.3 Transition Structures and Reaction Coordinates, 49 4.1.4 Molecular Dynamics Simulations, 49 4.2 Where the Target is Expressed, 50 4.3 Pharmacophore Identification, 50 4.4 Choosing an Inhibitor Mechanism, 51 Bibliography, 52 The Drug Design Process for a Known Protein Target 5.1 The Structure-Based Design Process, 53 5.2 Initial Hits, 55 5.3 Compound Refinement, 56 5.4 ADMET, 67 5.5 Drug Resistance, 67 Bibliography, 68 53 CONTENTS The Drug Design Process for an Unknown Target ix 71 6.1 The Ligand-Based Design Process, 71 6.2 Initial Hits, 72 6.3 Compound Refinement, 73 6.4 ADMET, 74 Bibliography, 74 Drug Design for Other Targets 75 7.1 DNA Binding, 76 7.2 RNA as a Target, 78 7.3 Allosteric Sites, 79 7.4 Receptor Targets, 80 7.5 Steroids, 81 7.6 Targets inside Cells, 82 7.7 Targets within the Central Nervous System, 83 7.8 Irreversibly Binding Inhibitors, 84 7.9 Upregulating Target Activity, 84 Bibliography, 85 Compound Library Design 87 8.1 8.2 8.3 8.4 8.5 Targeted Libraries versus Diverse Libraries, 87 From Fragments versus from Reactions, 89 Non-Enumerative Techniques, 90 Drug-Likeness and Synthetic Accessibility, 91 Analyzing Chemical Diversity and Spanning known Chemistries, 93 8.6 Compound Selection Techniques, 96 Bibliography, 99 PART II COMPUTATIONAL TOOLS AND TECHNIQUES Homology Model Building 9.1 How much Similarity is Enough?, 106 9.2 Steps for Building a Homology Model, 107 9.2.1 Step 1: Template Identification, 108 9.2.2 Step 2: Alignment between the Unknown and the Template, 108 103 105 x CONTENTS 9.2.3 Step 3: Manual Adjustments to the Alignment, 110 9.2.4 Step 4: Replace Template Side Chains with Model Side Chains, 111 9.2.5 Step 5: Adjust Model for Insertions and Deletions, 111 9.2.6 Step 6: Optimization of the Model, 112 9.2.7 Step 7: Model Validation, 112 9.2.8 Step 8: If Errors are Found, Iterate Back to Previous Steps, 115 9.3 Reliability of Results, 116 Bibliography, 117 10 Molecular Mechanics 119 10.1 A Really Brief Introduction to Molecular Mechanics, 119 10.2 Force Fields for Drug Design, 121 Bibliography, 123 11 Protein Folding 125 11.1 The Difficulty of the Problem, 125 11.2 Algorithms, 127 11.3 Reliability of Results, 129 11.4 Conformational Analysis, 130 Bibliography, 131 12 Docking 12.1 Introduction, 133 12.2 Search Algorithms, 135 12.2.1 Searching the Entire Space, 135 12.2.2 Grid Potentials versus Full Force Field, 137 12.2.3 Flexible Active Sites, 138 12.2.4 Ligands Covalently Bound to the Active Site, 138 12.2.5 Hierarchical Docking Algorithms, 139 12.3 Scoring, 141 12.3.1 Energy Expressions and Consensus Scoring, 141 12.3.2 Binding Free Energies, 141 12.3.3 Solvation, 144 12.3.4 Ligands Covalently Bound to the Active Site, 144 12.3.5 Metrics for Goodness of Fit, 144 12.4 Validation of Results, 145 12.5 Comparison of Existing Search and Scoring Methods, 146 12.6 Special Systems, 153 133 CONTENTS xi 12.7 The Docking Process, 155 12.7.1 Protein Preparation, 156 12.7.2 Building the Ligand, 156 12.7.3 Setting the Bounding Box, 157 12.7.4 Docking Options, 157 12.7.5 Running the Docking Calculation, 158 12.7.6 Analysis of Results, 158 Bibliography, 159 13 Pharmacophore Models 161 13.1 Components of a Pharmacophore Model, 163 13.2 Creating a Pharmacophore Model from Active Compounds, 164 13.3 Creating a Pharmacophore Model from the Active Site, 166 13.4 Searching Compound Databases, 167 13.5 Reliability of Results, 168 Bibliography, 169 14 QSAR 171 14.1 Conventional QSAR versus 3D-QSAR, 171 14.2 The QSAR Process, 172 14.3 Descriptors, 175 14.4 Automated QSAR Programs, 176 14.5 QSAR versus Other Fitting Methods, 177 Bibliography, 178 15 3D-QSAR 181 15.1 The 3D-QSAR Process, 182 15.2 3D-QSAR Software Packages, 184 15.3 Summary, 184 Bibliography, 184 16 Quantum Mechanics in Drug Design 16.1 Quantum Mechanics Algorithms and Software, 188 16.2 Modeling Systems with Metal Atoms, 191 16.3 Increased Accuracy, 191 16.4 Computing Reaction Paths, 193 16.5 Computing Spectra, 193 Bibliography, 194 187 292 GLOSSARY icon-oriented programming a process in which a computerized procedure can be defined by selecting graphically displayed boxes and connecting them by dragging a mouse to indicate the flow of data from one to the next identity the extent to which two primary sequences are identical, typically expressed as a percentage InChI (IUPAC International Chemical Identifier) a way of expressing chemical structures unambiguously, thus making it easy to determine computationally if two compounds are identical, enantiomers, tautomers, etc induced fit a process by which the shape of a protein’s active site changes depending upon the ligand bound in that site information content a mathematical measure of molecular complexity inhibition a reduction in the rate at which a protein performs its function in silico Latin for “in silicon,” meaning that it is a value generated by a computer program (computer chips are made from silicon) intellectual property a legal ownership of the rights to use and sell an idea or product, assigned based on a patent, copyright, or trademark intrinsic reaction coordinate (IRC) the lowest energy route from a transition structure to the reactants and products in a chemical reaction in situ Latin for “in the original position,” implying an experiment done in a living animal, or sometimes in live cells in vacuo Latin for “in vacuum,” indicating that a calculation or experiment is done without the presence of a solvent in vitro Latin for “in glass,” indicating a physical experiment done in a laboratory (in glassware, plate, etc.) Biochemical assays are considered in vitro assays Cell-based assays are sometimes considered in vitro and sometimes considered in situ Jarvis– Patrick a simple algorithm for nonhierarchical clustering Java a programming language made to run graphical interfaces on multiple platforms without recompiling Jython a Java implementation of the Python programming language Kier and Hall indices single numbers that describe the shape of a molecule K-means a nonhierarchical clustering algorithm knockout animals living animals that have been genetically manipulated to remove one open reading frame from their genome knowledge-based algorithm see heuristic algorithm Kohonen map a two-dimensional graphical representation of a multidimensional space of data GLOSSARY 293 lead a molecular structure motif that has been selected as the basis for more intensive drug design efforts lead hopping refers to a process that can find active molecular structure motifs that have not yet been identified for a given target lead-likeness sometimes synonymous with drug-likeness; sometimes based on more restrictive criteria than drug-likeness; sometimes based on physical properties of molecules, as opposed to drug-likeness being based on structural features library a list of compounds May refer either to physical samples of the compounds, or a list of data in a computer ligand-based drug design refers to drug design processes that can be used when the biomolecular target is unknown linear regression a mathematical process for getting the best possible fit of parameters to predict some property Linux a widely used computer operating system A public domain clone of the Unix operating system Lipinski rule of fives a set of criteria for predicting if a compound will be orally bioavailable lipophilic refers to the qualitative or quantitative property that a compound can be dissolved in lipids lock-and-key refers to a theory of drug design that the drug should fit in the target’s active site like a key fitting in a lock Manhattan distance a worst case measure of chemical similarity matrix least squares an efficient method for finding coefficients that allow parameters to be used to predict a property Mathematically equivalent to linear regression maximal common subgraph (MCS) refers to a pattern of atoms and bonds that an entire collection of compounds all contain mechanism refers to the detailed description of a chemical reaction This includes geometric motion of atoms, energetics, and sometimes vibrational motion memory refers to the amount of data that a computer can hold in silicon chips, without accessing the disk drive metabolic blocking refers to adding functional groups for the purpose of preventing a compound from being metabolized by a given mechanism in the body metabolic pathway the sequence of proteins and small molecules upon which they act that perform some function within the cell 294 GLOSSARY metabolization the process by which proteins alter compounds in order to use them or to remove them from the body metalloenzyme a protein with a metal atom in its active site metascale a modeling technique for working with systems (e.g., cells, membranes, or organs) that are too large to be modeled as a collection of individual atoms micromolar refers to a compound with a weak activity, of the order of 1026 MOGA (Multiobjective Genetic Algorithm) a specific process for performing multiobjective optimization molecular dynamics a simulation that shows how molecules move, vibrate, diffuse, and sometime react over time molecular electrostatic potential (MEP) the mathematical computation of regions around a molecule at which positive or negatively charged species will bind molecular mechanics a method for simulating molecules with the atoms represented as weights connected by springs and having a partial charge Monte Carlo algorithms test many possibilities based on a random number multidimensional optimization see multiobjective optimization multiobjective optimization refers to a process for finding a solution that is optimal in terms of multiple criteria multipole a representation of the charge distribution in a molecule, such as a dipole, quadrupole, octupole, etc mutagenicity the qualitative and quantitative tendency for a compound to cause mutations in the genome of cells, particularly during mitosis nanomolar refers to a compound with a potent activity, of the order of 1029 native substrate the species that would be acted upon by a protein in the course of the normal functioning of the body neural network an artificial intelligence algorithm that predicts something based on a simulations of the way in which the neurons interact in the brain neurotoxins compounds that are toxic owing to their ability to interrupt nerve impulses non-enumerative algorithm refers to a process that analyzes entire collections of molecules without ever creating a structure for each individual compound nonlinear map a way of displaying molecular similarity by representing similar molecules as points near one another on a map, and dissimilar compounds as points far apart GLOSSARY 295 NP-complete algorithm a class of mathematical problems that require a very complex, time-consuming computer algorithm to solve oral bioavailability the property of a drug that it can be swallowed in solid or liquid form and will be able to reach its intended target in the body paclitaxel an antitumor drug, similar to docetaxel Pareto optimization a multiobjective optimization algorithm analogous to a downhill energy search partial least squares a mathematical process for finding a way of predicting activity based on some collection of parameters passive intestinal absorption the process of a drug passing from the intestine into the bloodstream because it is sufficiently lipophilic to diffuse through the intestinal wall patent a legal document granting a person or company ownership and exclusive rights to sell something for a number of years pathogen a harmful virus or bacteria peptidomimetic a compound that is similar in shape to a peptide pharmacokinetic the properties of a drug in terms of its absorption, distribution, metabolism, and excretion pharmacophore an arrangement of interaction features (e.g., hydrogen bond donor/acceptor, steric, etc.) that can be used to search for compounds that might be active picomolar refers to a highly active compound, with activity of the order of 10212 primary sequence the order of amino acids in a protein chain, or nucleotides in a DNA or RNA chain principal components analysis weights of parameters that define a basis for prediction of activity privileged structures see promiscuous binders polarity indices numbers that describe the charge distribution in a molecule polarizability the readiness with which electron charge density shifts from one part of a molecule to another prodrug a compound that releases an active drug once it has been metabolized in the body promiscuous binders compounds that bind to many proteins or give false signals, thus causing them to test positive in assays for many different targets protein folding the process of attempting to compute the three-dimensional structure of a protein based on the primary sequence alone 296 GLOSSARY proteome the collection of all of the proteins in the body proteomics the study of the proteome Python an interpretive programming language QSAR “quantitative structure–activity relationship,” a way of predicting molecular properties quantum mechanics mathematical law of nature that describes the behavior of electrons, and thus chemical bonding Quantum Similarity Measure (QSM) a metric for comparing molecules Randic indices numbers that describe the shape of a molecule rational drug design see structure-based drug design reaction coordinate geometric and energetic description of a chemical reaction receptor a protein at the surface of a cell that passes a signal into the cell when an appropriate molecule binds to it Also, the active site of a protein, where a signaling molecule binds resistance change in a pathogen that makes it less susceptible to a drug resolution the accuracy of a crystallographic structure R-factor a numerical measure of the resolution of a crystallographic structure rotamers another name for conformers SAR “structure–activity relationship,” a qualitative description of some constraint on the molecular structure needed for activity scaffold hopping the finding of previously unknown structural motifs that are active against a given target Scientific Vector Language (SVL) a chemically aware programming language scoring (in docking) a numerical measure of how well a molecule binds to a target’s active site scoring plates the automated spectroscopic analysis of experimental assays screening the experimental measurement of compound activity Synonymous with assaying script language an interpretive computer programming language semiempirical quantum mechanical methods that substitute some of the most time-consuming integrals with parameterized values serotype a mutated form of a pathogen side effects unwanted physiological symptoms from taking a medication GLOSSARY 297 Silvadene (silver sulfadiazine) an antimicrobial drug similarity numerical measure of how closely two molecules compare with one another simulated annealing an algorithm for finding a near-optimal solution to a global optimization problem singleton a cluster with only one compound in it SLN “SYBYL Line Notation,” a full featured string format for representing chemical structure SMILES “Simplified Molecular Input Line Entry Specification,” a string format for representing two-dimensional chemical structure specificity the property of a drug that it inhibits the intended target, and not any other proteins in the body statistical mechanics the discipline that attempts to relate the properties of macroscopic systems to their atomic and molecular constituents strategic bonds chemical bonds that are targeted as the points to attach or remove functional groups as part of a synthesis route structurally conserved regions pieces of proteins, for which crystal structures are available, used to construct a homology model structural motif the central part of a drug’s chemical structure Many different functional groups will be added to this core region to generate focused libraries structural unit analysis an algorithm for generating structure–activity relationships structure-based drug design the process of designing drugs for a target for which the three-dimensional structure is known substrate a small molecule or small section of a large biomolecule that binds in a protein’s active site substrate analog a drug that is designed to look similar to a target’s native substrate substructure a piece of a chemical structure, typically used to search for other molecules containing the same pattern of atoms and bonds suicide inhibitor a drug that binds irreversibly to its target support vector machine a learning algorithm for creating a system to predict some property synthetic accessibility a measure of how easily a compound can be synthesized synthons a functional group or molecular fragment used to generate a combinatorial library 298 GLOSSARY tabu algorithm a process that searches a space (e.g., molecular positions in a docking calculation), but excludes sections of the space that have already been searched Tanimoto distance the most widely used measure of molecular similarity target the biomolecule in the body with which a drug is intended to interact targeted library see focused library template (in homology model building) a similar protein with a known three-dimensional structure, used to guide the creation of a homology model for the unknown protein teratogenicity a form of toxicity resulting in a malformed fetus if taken during pregnancy thalidomide a failed drug, which caused severe birth defects threading algorithm a process for aligning marginally similar protein structures for the purpose of creating a homology model time complexity the way in which the required CPU time for a calculation scales with the size of the problem tubulin a protein that forms microtubules during cell mitosis topical application of drug to the skin, for example in the form of a cream topological mathematical description of shape, such as connectivity, branching, and rings topological polar surface area (TPSA) a molecular descriptor frequently used to describe how a molecule will interact with a polar or nonpolar environment toxicity the quantitative quality of a compound doing harm to the body transgenic animals have an extra gene from another species inserted in their genome transition state/structure the three-dimensional structure and electronic structure of a molecule at the transition point (energy maximum) in a chemical reaction transition state analog a drug designed to look like the intermediate of the reaction of the enzyme that it is inhibiting uncompetitive inhibition occurs only when an inhibitor binds to the enzyme –substrate complex Unix a computer operating system used for workstations, servers, and supercomputers upregulating the process of a drug increasing the rate at which the target performs its action GLOSSARY 299 variable regions sections of a homology model for which no template is used Viagra (sildenafil) a drug for erectile dysfunction Ward’s algorithm a hierarchical clustering process Weiner index a number that describes the shape of a molecule INDEX 21 CFR part 11, 223 2D NMR, 43, 44– 45, 105, 125 3D Explorer, 57, 59 3D-QSAR, 18, 55, 66, 171 – 172, 176, 181– 184 ab initio calculation, 139, 189 absorption, 24, 172, 228, 229 Accelrys, 115, 147, 177, 184, 199, 222, 223, 242, 281– 282 ACD, 28, 88, 208, 243, 282 ACE, 228 active transport, 13, 82, 83, 227, 228, 253, 285 activity, 2, 10, 12, 16– 23, 54, 55, 78, 84– 85, 172, 173, 270, 285 activity cliff, 220, 221, 285 acute toxicity, 3, 232 ADMET, 16, 60, 65, 66, 67, 74, 225– 234, 238, 289 AI see artificial intelligence AIMB, 204 alignment, 20, 106, 108 – 110, 111, 115, 142, 147, 165, 168, 183, 249, 286 allosteric inhibitor, 79 AMBER, 122, 142, 146, 147, 150 Ames test, 25, 286 animal testing, 13, 14– 15, 270 anisotropy, 108, 111, 286 ant colony algorithm, 128 Antabuse, 34 antibiotic, 23, 24, 29, 32, 65, 67, 78, 228 application programming interface (API), 244 ARChem, 260, 261 artificial intelligence, 66, 197– 198, 204, 216, 260 assay, 12, 13, 18, 21, 25, 55, 73, 91, 96, 145, 161, 212, 220, 270 AutoDock, 141, 142, 143, 146, 147, 150, 152 backbone, 20, 55, 56, 67, 87, 90, 96, 112, 115, 116, 128, 130, 138, 182 Computational Drug Design By David C Young Copyright # 2009 John Wiley & Sons, Inc 301 302 INDEX BACON, 142, 150, 204 bacteria, 23, 32, 67, 68, 78 Bayes’ theorem, 202 BBB see blood –brain barrier BCI, 211 BCUT, 96 B-factor, 43, 108 bioavailability, 2, 13, 14, 16, 17, 23, 24– 25, 28, 33, 35, 50, 55, 56, 67, 82, 89, 135, 217, 228, 250, 253, 268, 270 biochemical assay, 3, 12, 16, 18, 145 biochemical pathway see metabolic pathway biofilm, 68 bioinformatics, 26, 51, 247– 251, 264 bioisosteric substitution, 58, 59, 60 BioSolveIT, 147 black list, 30 blood– brain barrier, 13, 29, 50, 63, 83, 172, 178, 220, 226, 231 –268, 269 Born–Oppenheimer approximation, 188 Botox, 34 Caco-2 assay, 13, 14, 82 CAESA, 91, 92, 260 Cambridge Crystallographic Data Centre, 134, 148 CAOS, 259, 260, 261 Carbo´ Similarity Index, 192 carcinogenicity, 25, 232 cardiotoxicity, 231, 232 CASP, 113, 116, 130 caspase, 18, 20, 79, 106, 113 CCG see Chemical Computing Group cell-based assay, 10, 11, 12, 13, 25, 270 cell simulation, 257 central nervous system, 29, 50, 66, 83– 84, 231, 278 chain extension, 59 ChemBasic, 243 Chemical Computing Group (CCG) cheminformatics, 3, 4, 91, 178, 207 –223, 243 cherry picking, 73 chronic toxicity, 3, 14 CIS, 194 cisplatin, 33, 76 Class Pharmer, 89, 220, 221, 222 clinical trials, 1, 15– 16, 26, 27, 67, 198, 232, 273, 274, 275 ClogP, 28, 29, 175, 239 cloning, 276 –277, 278 CNS see central nervous system combinatorial library, 30, 60, 66, 207 CoMFA, 183, 184 compound refinement, 56– 66, 67, 73 compound selection, 54, 72, 96 – 99, 141, 145, 161 Comprehensive Medicinal Chemistry (CMC), 30 CoMSIA, 184 CONCORD, 203, 204, 213 configuration interaction (CI), 189, 194 CONFLEX, 131, 283 conformational analysis, 130– 131, 169, 204 connectivity index, 175 consensus scoring, 141, 147, 152, 159 CORINA, 203, 204, 213 correlation matrix, 173 COSMO, 213, 226 COSMOlogic, 226, 254, 282 COSMO-RS, 213, 226 COSMOsim, 213, 282 COSMOthermX, 226, 227 cost, 1, 2, 4, 13, 14, 15, 26, 35, 55, 67, 72, 220, 250, 273, 274, 275 coupled cluster (CC), 189 covalently bound ligand, 66, 139, 144 CPU time, 112, 121, 126, 137, 138, 168, 189 crevice detection, 48, 167 cross reaction, 26, 27, 34 crystallography, 11, 42, 43 –44, 45, 48, 55, 105, 113, 149, 156 cytochrome P450, 23, 24, 26, 27, 30, 138, 229 cytotoxicity, 13, 25 INDEX database, 29, 30, 32, 41, 91, 93, 94, 156, 201, 208, 213, 222– 223, 259, 260 DataMiner, 98, 220, 222, 242 decision support software, 178, 217 dendrogram, 96, 97, 98, 214 de novo, 66, 132, 197, 198 – 201, 281 density functional theory (DFT), 189 DEREK, 201, 234 dielectric constant, 120, 121, 144 DISCOtech, 168 Discovery Studio, 82, 115, 147, 281, 282 distance (molecular similarity), 210, 212 distribution, 16, 60, 67, 74, 95, 97, 113, 175, 192, 226, 239 diverse library, 55, 73, 89, 96, 97 diversity, 93, 95, 178, 216 DNA, 10, 53, 76, 79, 154, 248, 250, 256, 259, 263, 273, 276 docetaxel, 162 DOCK, 142, 143, 146, 149, 150, 151, 152 docking, 3, 12, 18, 26, 50, 55, 56, 66, 79, 81, 133, 135, 144, 146, 148 downregulating, 14 drug candidate, 10, 35, 216 drug-likeness, 10, 27– 33, 91– 93, 239 drug resistance, 67– 68 DrugScore, 142, 149, 150, 152, 153 efficacy, 2, 10, 13, 14, 24, 26, 99, 267, 269 eHITS, 142, 149, 150, 283 encapsulated active site, 138 enrichment, 4, 145, 159 enterprise information system, 222, 223 entropy, 66, 141, 142, 143, 146, 148, 159 enzyme, 17, 33, 34, 51, 156, 157, 193, 228, 231, 255 epothilone, 162 excretion, 16, 60, 225 expert system, 32, 201, 203, 232, 234, 260 303 extension of structure, 58, 60 extra precision (XP), 148 FDA see Food and Drug Administration Fen-Phen, 27, 231 FEP see free energy perturbation fingerprints, 32, 94, 95, 210, 211, 212 FlexX, 142, 147, 150, 152 Flo, 143, 148, 151 Food and Drug Administration (FDA), 15 force field see molecular mechanics Fortran, 146 FRED, 142, 149, 150, 152 free energy perturbation, 139, 141, 143, 193 frequent hitter see promiscuous binder functional groups, 2, 9, 23, 27, 29, 30, 56, 59, 60, 67, 76, 91, 92, 95, 158, 198, 200, 212, 267 fuzzy logic, 202 GastroPlus, 231, 256 Gaucher’s disease, 33, 34 Gaussian, 139, 142, 149, 184, 190, 194 GB/SA, 142, 146, 150 genetic algorithm, 147, 148, 177, 239 genetic manipulation, 14, 275– 276, 278 genome, 1, 68, 247, 248, 251, 255, 273 genome sequencing, 248, 273 – 274 geometry optimization, 49, 239 Glide, 140, 142, 148, 150, 152, 159 GLUT2, 228 GOLD, 134, 142, 148, 152, 159 G-protein-coupled receptor, 75, 76 GPCR see G-protein-coupled receptor graphical user interface (GUI), 244 grid potential, 137 – 138, 148, 157 grid search, 112, 126, 127 group additivity, 90, 177, 178, 228, 234 group average clustering, 215 Gue´noche clustering, 215, 216 304 INDEX half-life, 4, 89, 229 – 231 HASL, 184 hepatotoxicity, 24, 232 hERG, 11, 232 heterocycle, 64 heuristic, 32, 128, 176, 199, 260 hierarchical clustering, 96, 97, 214 hierarchical docking, 139– 140 high throughput screening (HTS), 12, 54, 207, 218, 222 high throughput virtual screening (HTVS), 148 hit, 10, 11, 60, 261 HOMO, 176, 192 homology, 19, 42, 43, 44, 45, 105, 106, 109, 111, 115, 249 homology model, 42, 43, 45, 105– 117, 125, 128, 129, 248, 249 HookSpace, 98 hPEPT1, 227, 228 HQSAR, 178, 211, 212 human insulin receptor, 83 IC50, 10, 12, 164 ICM, 143, 148, 150, 159 icon-oriented programming, 242 InChI, 208, 209 induced fit, 19, 21, 47, 48, 49, 66, 81, 138 information content, 93, 175 inhibition constant (KI), 12, 16, 18 in situ assay intellectual property, 2, 41, 46, 51 intrinsic reaction coordinate see reaction coordinate in vitro assay, 12, 13 in vivo assay IRC see reaction coordinate isoCYP, 231 Jarvis – Patrick clustering, 215, 216 Java, 242, 243 Jython, 242 Kier and Hall index, 175 kinetics, 11, 20, 48, 49, 54, 66, 125 K-means clustering, 215 knockout animals, 14 knowledge-based see heuristic Kohonen map, 98, 99 LASSO, 213 LBDD see ligand-based drug design LD50, 25, 232 lead, 11, 75, 86, 88, 199, 231, 237, 263 lead hopping, 73 lead-likeness see drug-likeness leave one out, 183 LHASA, 93, 202, 260 ligand-based drug design, 71, 72, 73, 74, 184 LigandFit, 143, 147 LigandScout, 168 LigScore, 143, 147, 151, 152, 153, 159 linear regression, 146, 177, 204 Linux, 243, 251, 281 Lipinski rules, 28, 29, 238 LIQUID, 168 lock-and-key, 17, 76 log D, 11, 28, 228, 282 log P, 28, 30, 83, 95, 228, 231, 282 log S, 29, 239 log Sw, 29 LOO see leave one out LUDI, 143, 151, 199 LUMO, 176, 192 MAb see monoclonal antibody MACCS, 211, 212 Manhattan distance, 211 matrix least squares, 173, 177, 204 maximal common substructure (MCS), 214 MCASE, 234 MDL, 29, 211, 223 MDR1-MDCK assay, 14 membrane, 13, 14, 78, 79, 82, 227, 228, 254, 264, 270 memory, 90, 126, 128, 137, 189, 241 MEP see molecular electrostatic potential metabolic blocking, 67 INDEX metabolic pathway, 41– 43, 50, 51, 79, 84, 85, 153, 154, 251, 254 metabolization, 16, 60, 67, 74, 225 metalloenzyme, 182, 191 metascale, 294 mixed inhibition, 51 MlogP, 28, 175 MLP see molecular lipophilic potential MOE, 18, 115, 142, 143, 151, 165, 173, 242, 282 molecular electrostatic potential, 193 molecular lipophilic potential, 183 molecular shape analysis, 183 molecular weight, 2, 25, 29, 83, 91, 94, 95, 98, 171, 216, 231 Møller – Plesset, 189 MolSoft, 148 monoclonal antibody, 83, 270 MoSELECT, 239 MOZYME, 190 MRSA, 67 MSA see molecular shape analysis Multiobjective Genetic Algorithm, 239 multiobjective optimization, 237– 240 multiple drug interactions, 26 –27 multipole, 175 mutagenicity, 13, 25, 232, 233 native substrate, 17, 49, 51, 65, 157, 193 Netropsin, 77 neural network, 32, 172, 178, 197, 204, 205, 226 neurotoxins, 25, 33 NOE see 2D NMR noncompetitive inhibition, 51 nonlinear map, 98, 99, 211, 217 NP-complete, 165 nuclear Overhauser effect see 2D NMR OCSS, 202, 259, 260 OMEGA, 149 OpenEye Scientific Software, 149, 168 open reading frame, 1, 263 oral bioavailability, 13, 16, 24, 28, 33, 55, 56, 227 – 228, 253 organ simulation, 256 305 paclitaxel, 79, 162 pairSAR, 220, 221 PAMPA assay, 14 Pareto optimization, 239 partial least squares, 181, 183 passive intestinal absorption, 28, 32, 95, 178, 227, 228, 268 pathogen, 65, 66, 68, 158 PB/SA, 146 PCA see principal components analysis penicillin, 56, 57, 58, 68, 84 peptidomimetic, 59, 65 pharmacokinetics, 11, 54, 55, 56, 76, 79, 82, 218, 237 pharmacophore, 12, 18, 21, 50– 51, 55, 66, 81, 95, 130, 135, 140, 142, 145, 162, 163, 164, 165, 166 – 167, 168, 181, 201 phylogenetic tree, 248, 250 Pipeline Pilot, 242 pKa, 192, 208 PLP, 143, 147, 149, 153, 159 PLS see partial least squares PMF see potential of mean force polarity index, 175 polarizability, 94, 96, 175 posttranslational modification, 263 potential of mean force, 113, 141, 147 principal components analysis, 94, 99 privileged structure see promiscuous binder prodrug, 32, 55, 83, 228, 267 – 270 PRO_LIGAND, 201 promiscuous binders, 21 protein classification, 19 protein folding, 43, 45–46, 117, 125–131 proteome, 1, 26, 263, 264, 274 proteomics, 263 – 264 Python, 242 Q-Chem, 190, 194 QM/MM, 49, 139, 190, 193 QPLD, 143, 190, 191 QSAR, 12, 25, 99, 171– 178, 182, 184, 192, 203, 207, 220, 226, 231, 232, 234, 254 306 INDEX quantum mechanics, 119, 120, 187, 188, 191, 193, 194 Quantum Similarity Measure, 192 QXP, 143, 148, 151, 152 Ramachandran plot, 113 Randic index, 175 rational drug design see structure-based drug design reaction coordinate, 4, 49, 193 receptor, 80, 81, 83, 85, 146, 147 retroisosterism, 60, 65 R-factor, 108 ring expansion, 59 RNA, 10, 53, 78–79, 153, 154, 250, 251 ROCS, 143, 151, 159, 168 ROSDAL, 208 rotamer, 112, 113, 115 SAR see structure – activity relationship SBDD see structure-based drug design scaffold hopping, 161, 182 Schroădinger equation, 120, 188, 189 Schroădinger Inc., 116, 139, 140, 141, 148, 190 Scitegic, 242, 281 scoring assay plates, 12 scoring docking poses, 148, 156 screening, 4, 10, 12, 73, 93, 142, 145, 207, 210, 218, 219 script language, xxxv SDS, 259 SECS, 202, 260 semiempirical, 119, 139, 189, 190 serotype, 19, 65, 66 side effects, 14, 15, 16, 19, 21, 26, 27, 34, 50, 66, 76, 84, 144, 232, 237, 274 sildenafil see Viagra Silvadene, 35 SimBioSys, 91, 92, 199, 200, 201, 213, 261, 283 similarity of molecules, 289 similarity of primary sequence, 41, 43, 45, 125, 126, 128 simplification of structure, 59 simulated annealing, 127, 136, 137, 147 Simulations Plus, 89, 220, 222, 226, 230, 231, 256 singleton, 98, 215, 216, 218, 220 SLN, 203, 208, 209, 210, 213, 223 SMILES, 203, 208, 209 solubility, 11, 29, 228 solvation, 66, 120, 126, 141, 143, 144, 146, 157 Spartan, 188 specificity, 18, 20, 26, 33, 35, 47, 51, 54, 60, 66, 76, 269 specificity pocket, 19, 20, 56, 167 spreadsheet, 177, 208, 213, 241, 242 SPROUT, 199 SPROUT-LeadOpt, 201 standard precision (SP), 148 stem cells, 248, 264, 276, 277 – 278 steroid, 29, 81 –82, 154 strategic bonds, 93 streptomycin, 78 structurally conserved region, 108 structural motif, 30, 31, 56, 94, 208, 210, 218, 220 structural unit analysis, 220, 221, 222 structure – activity relationship (SAR), 11, 60, 73, 171, 203, 212, 218 structure-based drug design, 3, 18, 43, 51, 53, 54, 55, 56, 65, 66, 68, 73, 74, 159, 198 substrate analog, 17, 59, 65 substructure, 56, 161, 169, 210–213, 220 suicide inhibitor, 24, 51, 84, 139, 192 support vector machine, 32, 177, 204 Surflex, 143, 151, 152, 159 SVL, 148, 243 synSPROUT, 200 synthetic accessibility, 91– 93, 200, 218, 219 synthon, 87, 88, 90 Tanimoto distance, 94, 210, 211 target, 10, 12, 13, 25, 26, 42, 43, 46, 47, 48, 50– 51, 53, 55, 60, 65, 66, 71, 72, 74, 78– 79, 108, 129, 181, 183, 198, 201, 218, 260, 268 INDEX targeted library, 87 taxane, 50, 162 thalidomide, 23 Thistlesoft, 148 time complexity, 127, 190, 215 topical application, 35 Topkat, 233, 234 toxicity, 2, 3, 14, 16, 25, 27, 32, 34, 35, 55, 60, 79, 89, 177, 225, 231– 234, 239 TPSA, 175 transferrin receptor, 83 transgenic animals, 14 transition state, 17, 49, 59, 65 transition state analog, 17, 59, 65 transition structure, 17, 49 Trident, 187, 188 Tripos, 57, 59, 98, 115, 122, 147, 168, 177, 184, 208, 214, 220, 222, 223, 242, 243, 283 tubulin, 50, 79, 162 uncompetitive inhibition, 51 UNITY, 210, 212, 223, 243 Unix, 223, 243 upregulating target activity, 84 – 85 variable region, 108 variation of substituents, 58, 60 Viagra, 34, 35 virus, 10, 19, 67 Vitravene, 79, 250 Ward clustering, 215 Wavefunction Inc., 188 Weiner index, 175 WLN, 208 XP see extra precision X-ray crystallography see crystallography X-Score, 143, 149, 151, 152, 159 307 ... 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