Reviews in Computational Chemistry Volume 18

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CHAPTER 2 The Use of Scoring Functions in Drug Discovery Applications Hans-Joachim Böhm and Martin Stahl F. Hoffmann-La Roche AG, Pharmaceuticals Division, Chemical Technologies, CH-4070 Basel, Switzerland INTRODUCTION Reviews in Computational Chemistry, Volume 18 E dited by K enny B. L ipkowitz and D onald B. Boyd Copyr ight © 2002 John Wiley & Sons, I nc. ISBN: 0-471-21576-7 Structure-based design has become a mature and integral part of medicinal chemistry. It has been convincingly demonstrated for a large number of targets that the three-dimensional (3D) structure of a protein can be used to design small molecule ligands binding tightly at this target. Indeed, several marketed compounds can be attributed to a successful structure-based design. 1–4 Several reviews summarize these results. 5–9 Since the introduction of molecular modeling and structure-based design into the drug discovery process in the 1980s, there has been a significant change in the role these computational techniques are playing. Early molecular modeling work concentrated on the manual design of protein ligands using the 3D structure of a target. Usually, the creativity of the designer was used to build a novel putative ligand using computer graphics followed by a molecular mechanics calculation of the resulting protein–ligand complex. A geometric and energetic analysis of the energy-minimized complex was then used to assess the putative ligand. A good complementarity of the shape and surface properties between the protein and ligand was used as an indication that the ligand might indeed bind to the protein with high affinity. 41
42 The Use of Scoring Functions in Drug Discovery Applications However, designing a single, active, synthetically accessible compound turned out to be a greater challenge than expected. It is still difficult to computationally predict induced-fit phenomena and binding affinities for new ligand candidates. But while existing modeling tools are certainly not suitable to design the one perfect drug molecule, they can help to enrich sets of molecules with greater numbers of biologically active candidates, even though the rates of false positives (and false negatives) are still high. Thus, an important current goal of molecular design is to increase the hit rate in biological assays compared to random compound selections, which means that structure-based design approaches now focus on the processing of large numbers of molecules. These ‘‘virtual libraries’’ of molecules can consist of either existing molecules (e.g., the compound collection of a pharmaceutical company) or of putative novel structures that could be synthesized via combinatorial chemistry. The computational goal is to rapidly assess millions of possible molecules by filtering out the majority that are predicted to be extremely unlikely to bind, and then to prioritize the remaining ones. This approach is, in fact, a successful strategy, and several recent publications have demonstrated impressive enrichment of active compounds. 10–15 The change of focus from individual compounds to compound libraries has been supported by three major developments that have taken place since the early days of molecular design: 1. An exponentially growing number of 3D protein structures is available in the public domain. Consequently, the number of projects relying on structural information has increased, and structure-based ligand design is nowadays routinely carried out at all major pharmaceutical companies. The amount of structural knowledge is so large that automated methods are needed to make full use of it. 2. High throughput screening (HTS) has become a well-established process. Large libraries of several hundred thousand compounds are routinely tested against new targets. This biological testing can, in many cases, be carried out in less than one month. 3. Synthetic chemistry has undergone a major change with the introduction of combinatorial and parallel chemistry techniques. There is a continuous trend to move away from the synthesis of individual compounds toward the synthesis of compound libraries, whose members are accessible through the same chemical reaction using different chemical building blocks. To offer a competitive advantage, structure-based design tools must now be fast enough to prioritize thousands of compounds per day. Several algorithms have been developed that allow for de novo design 16,17 or for flexible docking 18 of hundreds to thousands of small molecules into a protein binding site per day on a single CPU computer. Essential components of all these structure-based design software tools are scoring functions that translate computationally determined protein–ligand interactions into approximate
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Reviews in Computational Chemistry
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Kenny B. Lipkowitz Department of Ch
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vi Preface three-dimensional struct
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viii Preface some descriptors and i
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Epilogue and Dedication My associat
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Contents 1. Clustering Methods and
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Contents xv Electron Transfer in Po
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Contributors John M. Barnard, Barna
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Contributors to Previous Volumes *
Page 19 and 20: Volume 3 (1992) Tamar Schlick, Opti
Page 21 and 22: Volume 7 (1996) Geoffrey M. Downs a
Page 23 and 24: Volume 11 (1997) Mark A. Murcko, Re
Page 25 and 26: T. Daniel Crawford* and Henry F. Sc
Page 27 and 28: Topics Covered in Volumes 1-18 * Ab
Page 29 and 30: Reviews in Computational Chemistry
Page 31 and 32: 2 Clustering Methods and Their Uses
Page 39 and 40: 10 Clustering Methods and Their Use
Page 69: 40 Clustering Methods and Their Use
Page 73 and 74: 44 The Use of Scoring Functions in
Page 79 and 80: Table 1 Reference List for the Most
Page 117 and 118: CHAPTER 3 Potentials and Algorithms
Page 119 and 120: Vn (1 + cos(nω + γ)) 2 K θ (θ
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are modified by their environment w
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Table 1 Polarizability Parameters f
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The polarizable point dipole models
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two to three orders of magnitude sl
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M i z i + q i d i k i and shell cha
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Shell Models 103 on estimates of sh
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minimization can be replaced by mor
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The energy required to create a cha
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for all i ði:e:; 8 iÞ: @U @qi l
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where we have used q Cl ¼ qNa. The
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Electronegativity Equalization Mode
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Electronegativity Equalization Mode
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of N molecules is taken as a Hartre
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is treated using variable charges.
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water have been developed, includin
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Applications 123 classical and rigi
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developing polarizable models. A va
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Comparison of the Polarization Mode
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negligible errors in such propertie
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ecome significant at field strength
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noteworthy in this regard because t
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References 135 9. P. Cieplak and P.
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References 137 48. E. L. Pollock an
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References 139 Computational Chemis
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References 141 139. J. Hinze and H.
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References 143 178. J. J. P. Stewar
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References 145 216. M. W. Mahoney a
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CHAPTER 4 New Developments in the T
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Introduction 149 applications). For
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FCWD(∆E ) FCWD(0) ∆E = 0 ∆E
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Introduction 153 Equations [6]-[12]
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Paradigm of Free Energy Surfaces 15
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Paradigm of Free Energy Surfaces 15
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In Eqs. [18] and [19], F0i is the e
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where ‘‘þ’’ and ‘‘ ’
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is, however, small for the usual co
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solute-solvent coupling through the
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where Z is the electrode overpotent
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energy surfaces of ET. 33,50-56 It
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This result indicates a fundamental
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βF i (X ) 40 20 0 −20 1 2 βλ 1
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Table 1 Main Features of the Two-Pa
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and Hss ¼ U rep ss 1 2 X j;k ðmj
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λ i /eV 4 3 2 1 0 0 10 20 30 40 Bo
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the width/Stokes shift relation (Eq
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Table 3 Mapping of the Q Model on S
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This situation is of course not sat
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F ± (Y ad )/λ I 0.8 0.4 0 −0.4
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it by choosing the GMH basis set 7
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Anharmonic higher order terms gain
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of the radiation is the perturbatio
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individual vibrational excitations
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m 12 /D 6 5.5 5 4.5 4 3.5 17 18 19
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In Eq. [144], the coordinates Y km
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ε/M −1 cm −1 4000 2000 0 analy
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βσ 2 /10 3 cm −1 14 12 10 8 Opt
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0.2 0.1 0 12 16 20 24 28 32 The app
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References 207 7. M. D. Newton, Adv
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References 209 59. R. Kubo and Y. T
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Reviews in Computational Chemistry Volume 18

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