Reviews in Computational Chemistry Volume 18

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References 81 88. I. Muegge, Med. Chem. Res., 9, 490 (1999). The Effect of Small Changes in Protein Structure on Predicted Binding Modes of Known Inhibitors of Influenza Virus Neuraminidase: PMF- Scoring in DOCK4. 89. I. Muegge, Perspect. Drug Discovery Design, 20, 99 (2000). A Knowledge-Based Scoring Function for Protein–Ligand Interactions: Probing the Reference State. 90. I. Muegge, J. Comput. Chem., 22, 418 (2001). Effect of Ligand Volume Correction on PMF Scoring. 91. M. Stahl, Perspect. Drug Discovery Design, 20, 83 (2000). Modifications of the Scoring Function in FlexX for Virtual Screening Applications. 92. H. Gohlke, M. Hendlich, and G. Klebe, J. Mol. Biol., 295, 337 (2000). Knowledge-Based Scoring Function to Predict Protein–Ligand Interactions. 93. H. Gohlke, M. Hendlich, and G. Klebe, Perspect. Drug Discovery Design, 20, 115 (2000). Predicting Binding Modes, Binding Affinities and ‘‘Hot Spots’’ for Protein-Ligand Complexes Using a Knowledge-Based Scoring Function. 94. E. C. Meng, B. K. Shoichet, and I. D. Kuntz, J. Comput. Chem., 13, 505 (1992). Automated Docking With Grid-Based Energy Evaluation. 95. I. D. Kuntz, J. M. Blaney, S. J. Oatley, R. Langridge, and T. Ferrin, J. Mol. Biol., 161, 269 (1982). A Geometric Approach to Macromolecule–Ligand Interactions. 96. B. K. Shoichet, D. L. Bodian, and I. D. Kuntz, J. Comput. Chem., 13, 380 (1992). Molecular Docking Using Shape Descriptors. 97. E. C. Meng, D. A. Gschwend, J. M. Blaney, and I. D. Kuntz, Proteins: Struct., Funct., Genet., 17, 266 (1993). Orientational Sampling and Rigid-Body Minimization in Molecular Docking. 98. T. J. A. Ewing and I. D. Kuntz, J. Comput. Chem., 18, 1175 (1997). Critical Evaluation of Search Algorithms for Automated Molecular Docking and Database Screening. 99. S. Makino and I. D. Kuntz, J. Comput. Chem., 18, 1812 (1997). Automated Flexible Ligand Docking Method and Its Application for Database Search. 100. M. Vieth, J. D. Hirst, A. Kolinski, and C. L. Brooks III, J. Comput. Chem., 19, 1612 (1998). Assessing Energy Functions for Flexible Docking. 101. G. M. Morris, D. S. Goodsell, R. S. Halliday, R. Huey, W. E. Hart, R. K. Belew, and A. J. Olson, J. Comput. Chem., 14, 1639 (1998). Automated Docking Using a Lamarckian Genetic Algorithm and an Empirical Binding Free Energy Function. 102. M. Schapira, M. Trotov, and R. Abagyan, J. Mol. Recognition, 12, 177 (1999). Prediction of the Binding Energy for Small Molecules, Peptides and Proteins. 103. B. K. Shoichet, A. R. Leach, and I. D. Kuntz, Proteins: Struct., Funct., Genet., 34, 4 (1999). Ligand Solvation in Molecular Docking. 104. B. N. Dominy and C. L. Brooks III, Proteins: Struct., Funct., Genet., 36, 318 (1999). Methodology for Protein–Ligand Binding Studies: Application to a Model for Drug Resistance, the HIV/FIV Protease System. 105. X. Zou, Y. Sun, and I. D. Kuntz, J. Am. Chem. Soc., 121, 8033 (1999). Inclusion of Solvation in Ligand-Binding Free Energy Calculations Using the Generalized Born Model. 106. I. Massova and P. A. Kollman, J. Am. Chem. Soc., 121, 8133 (1999). Computational Alanine Scanning to Probe Protein–Protein Interactions: A Novel Approach to Evaluate Binding Free Energies. 107. O. A. T. Donini and P. A. Kollman, J. Med. Chem., 43, 4180 (2000). Calculation and Prediction of Binding Free Energies for the Matrix Metalloproteinases. 108. B. Kuhn and P. A. Kollman, J. Am. Chem. Soc., 122, 3909 (2000). A Ligand That is Predicted to Bind Better to Avidin Than Biotin: Insights From Computational Fluorine Scanning. 109. J. Aquist, C. Medina, and J.-E. Samuelsson, Protein Eng., 7, 385 (1994). A New Method for Predicting Binding Affinity in Computer-Aided Drug Design. 110. T. Hansson, J. Marelius, and J. Aquist, J. Comput.-Aided Mol. Design, 12, 27 (1998). Ligand Binding Affinity Prediction by Linear Interaction Energy Methods.
82 The Use of Scoring Functions in Drug Discovery Applications 111. I. D. Wall, A. R. Leach, D. W. Salt, M. G. Ford, and J. W. Essex, J. Med. Chem., 42, 5142 (1999). Binding Constants of Neuraminidase Inhibitors: An Investigation of the Linear Interaction Energy Method. 112. R. C. Rizzo, J. Tirado-Rives, and W. L. Jorgensen, J. Med. Chem., 44, 145 (2001). Estimation of Binding Affinities for HEPT and Nevirapine Analogues With HIV-1 Reverse Transcriptase via Monte Carlo Simulations. 113. D. A. Pearlman, J. Med. Chem., 42, 4313 (1999). Free Energy Grids: A Practical Qualitative Application of Free Energy Perturbation to Ligand Design Using the OWFEG Method. 114. D. A. Pearlman and P. A. Charifson, J. Med. Chem., 44, 502 (2001). Improved Scoring of Ligand–Protein Interactions Using OWFEG Free Energy Grids. 115. D. N. A. Boobbyer, P. J. Goodford, P. M. McWhinnie, and R. C. Wade, J. Med. Chem., 32, 1083 (1989). New Hydrogen-Bond Potentials for Use in Determining Energetically Favorable Binding Sites on Molecules of Known Structure. 116. R. C. Wade, K. J. Clark, and P. J. Goodford, J. Med. Chem., 36, 140 (1993). Further Development of Hydrogen-Bond Functions for Use in Determining Energetically Favorable Binding Sites on Molecules of Known Structure. 1. Ligand Probe Groups With the Ability to Form Two Hydrogen Bonds. 117. R. C. Wade and P. J. Goodford, J. Med. Chem., 36, 148 (1993). Further Development of Hydrogen Bond Functions for Use in Determining Energetically Favorable Binding Sites on Molecules of Known Structure. 2. Ligand Probe Groups With the Ability to Form More Than Two Hydrogen Bonds. 118. M. K. Gilson, J. A. Given, B. L. Bush, and J. A. McCammon, Biophys. J., 72, 1047 (1997). The Statistical-Thermodynamic Basis for Computation of Binding Affinities: A Critical Review. 119. T. P. Straatsma, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., VCH Publishers, New York, 1991, Vol. 9, pp. 81–127. Free Energy by Molecular Simulation. 120. P. A. Kollman, Acc. Chem. Res., 29, 461 (1996). Advances and Continuing Challenges in Achieving Realistic and Predictive Simulations of the Properties of Organic and Biological Molecules. 121. M. L. Lamb and W. L. Jorgensen, Curr. Opin. Chem. Biol., 1, 449 (1997). Computational Approaches to Molecular Recognition. 122. M. R. Reddy, M. D. Erion, and A. Agarwal, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York, 2000, Vol. 16, pp. 217– 304. Free Energy Calculations: Use and Limitations in Predicting Ligand Binding Affinities. 123. M. K. Gilson, J. A. Given, and M. S. Head, Chem. Biol., 4, 87 (1997). A New Class of Models for Computing Receptor–Ligand Binding Affinities. 124. A. E. Mark and W. F. van Gunsteren, J. Mol. Biol., 240, 167 (1994). Decomposition of the Free Energy of a System in Terms of Specific Interactions. 125. D. Williams and B. Bardsley, Perspect. Drug Discovery Design, 17, 43 (1999). Estimating Binding Constants—The Hydrophobic Effect and Cooperativity. 126. P. R. Andrews, D. J. Craik, and J. L. Martin, J. Med. Chem., 27, 1648 (1984). Functional Group Contributions to Drug–Receptor Interactions. 127. H.-J. Schneider, Chem. Soc. Rev., 227 (1994). Linear Free Energy Relationships and Pairwise Interactions in Supramolecular Chemistry. 128. T. J. Stout, C. R. Sage, and R. M. Stroud, Structure, 6, 839 (1998). The Additivity of Substrate Fragments in Enzyme–Ligand Binding. 129. M. K. Holloway, J. M. Wai, T. A. Halgren, P. M. D. Fitzgerald, J. P. Vacca, B. D. Dorsey, R. B. Levin, W. J. Thompson, L. J. Chen, S. J. deSolms, N. Gaffin, T. A. Lyle, W. A. Sanders, T. J. Tucker, M. Wiggins, C. M. Wiscount, O. W. Woltersdorf, S. D. Young, P. L. Darke, and J. A. Zugay, J. Med. Chem., 38, 305 (1995). A Priori Prediction of Activity for HIV-Protease Inhibitors Employing Energy Minimization in the Active Site.
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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 *
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Volume 3 (1992) Tamar Schlick, Opti
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Volume 7 (1996) Geoffrey M. Downs a
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Volume 11 (1997) Mark A. Murcko, Re
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T. Daniel Crawford* and Henry F. Sc
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Topics Covered in Volumes 1-18 * Ab
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Reviews in Computational Chemistry
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2 Clustering Methods and Their Uses
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10 Clustering Methods and Their Use
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Page 59 and 60: 30 Clustering Methods and Their Use
Page 71 and 72: 42 The Use of Scoring Functions in
Page 79 and 80: Table 1 Reference List for the Most
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Page 117 and 118: CHAPTER 3 Potentials and Algorithms
Page 119 and 120: Vn (1 + cos(nω + γ)) 2 K θ (θ
Page 121 and 122: are modified by their environment w
Page 123 and 124: Table 1 Polarizability Parameters f
Page 125 and 126: The polarizable point dipole models
Page 127 and 128: two to three orders of magnitude sl
Page 129 and 130: M i z i + q i d i k i and shell cha
Page 131 and 132: Shell Models 103 on estimates of sh
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Page 135 and 136: The energy required to create a cha
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Page 139 and 140: where we have used q Cl ¼ qNa. The
Page 141 and 142: Electronegativity Equalization Mode
Page 143 and 144: Electronegativity Equalization Mode
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Page 149 and 150: water have been developed, includin
Page 151 and 152: Applications 123 classical and rigi
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Page 155 and 156: Comparison of the Polarization Mode
Page 157 and 158: negligible errors in such propertie
Page 159 and 160: 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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