Reviews in Computational Chemistry Volume 18

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Critical Assessment of Current Scoring Functions 61 from these interactions to conventional interaction terms, which may be one more reason why conventional hydrogen-bond contributions have traditionally been overestimated. One could imagine adding terms to empirical scoring functions that are omitted in the calibration of the functions, but adjusted empirically to reward especially good fits, in a way analogous to penalty terms. Knowledge-based methods would also allow one to incorporate these interactions in a scoring function, again provided that directionality is taken into account, which is not the case in current approaches. Water Structure and Protonation State Uncertainties about protonation states and water structure at the receptor–ligand interface also make scoring difficult. These effects play a role in the derivation as well as in the application of scoring functions. The entropic and energetic contributions of water reorganization upon ligand binding are very difficult to predict (see, e.g., Ref. 180). The only reasonable approach for addressing this problem is to concentrate on conserved water molecules and make them part of the receptor. For example, the docking program FLOG has been applied to the search of inhibitors for a metallo-b-lactamase 13 within the Merck in-house database. Docking was performed with three different configurations of bound water in the active site. The top-scoring compounds showed an enrichment in biphenyl tetrazoles, several of which were found to be active at a concentration below 20 mM. A crystal structure of one tetrazole (IC50 ¼ 1.9 mM) not only confirmed the predicted binding mode of one of the inhibitors, but also displayed the water configuration that had— retrospectively—been the most predictive one of the three models. Scoring functions rely on a fixed assignment of a general atom type to each protein and ligand atom. This also implies a fixed assignment of protonation state for each acidic and basic functional group. Even though these assignments can be reliable enough for conditions in aqueous solution, significant pKa shifts can be witnessed upon ligand binding. 181 This phenomenon can arise from local changes of dielectric conditions inside the binding pocket. The change of a donor to an acceptor functionality due to modified protonation states has important consequences for scoring. 137 Accordingly, improved docking and scoring algorithms will eventually need to have a more detailed and flexible description of protonation states. Performance in Structure Prediction The multitude of different solutions that have been used for receptor– ligand scoring calls for an objective assessment that could help future users to decide which function to use under a given set of circumstances. To do this, one must differentiate between predicting protein–ligand complex structures
62 The Use of Scoring Functions in Drug Discovery Applications (i.e., the scoring function is used as the objective function in docking), rank ordering a set of ligands with respect to the same protein (Ki prediction), and the use of scoring functions to discover weakly binding compounds from a large database of mostly nonbinders (virtual screening). Note that the latter two tasks are indeed very different. In virtual screening, the focus is on elimination of nonbinders, whereas the correct rank order of weakly, medium, and strongly binding molecules is of secondary interest. Even when the criteria are clear, a comprehensive assessment of scoring functions is difficult because very few functions have been tested on the same data sets. For example, studies where each scoring function is used in conjunction with two different docking algorithms (e.g., Ref. 170) are not meaningful in this context, because each docking algorithm produces different sets of solution structures. For structure prediction, several studies have shown that knowledge-based scoring functions are at least as good as empirical functions. They are somewhat ‘‘softer’’ than empirical functions, 162 meaning that small root-mean-square deviations from the crystal structure usually do not lead to huge changes in score, a fact that can mainly be attributed to the neglect of directionality. The PMF function has been successfully applied to structure prediction of inhibitors of neuraminidase 88 and stromelysin 1 (matrix metalloprotease-3; MMP-3) 182 in the program DOCK, yielding superior results to the DOCK force field and chemical scoring options. The DrugScore function was tested on a large set of PDB complexes and gave significantly better results than the standard FlexX scoring function with FlexX as the docking engine. DrugScore performed as well as the force field score in DOCK, but outperformed chemical scoring. Grueneberg, Wendt, and Klebe 15 used the Drug- Score function in a virtual screening study to find novel carbonic anhydrase inhibitors (see the section on Application of Scoring Functions in Virtual Screening later in this chapter). Two of the virtual hits that turned out to be highly active compounds were then examined crystallographically. The docking solution predicted by DrugScore was closer to the experimental structure than that predicted by the FlexX score. Although the objective function (the function whose global minimum is searched during docking) is used for both structure generation and energy evaluation in many docking programs, better results can often be obtained if different functions are used. More specifically, the docking objective function can be adapted to the docking algorithm used. In a parameter study, Vieth et al. 100 found that by using a soft-core van der Waals potential their MD-based docking algorithm became more efficient. Using FlexX as the docking engine, we observed that when directed interactions (mostly hydrogen bonds) are emphasized in the docking phase, library ranking can be done successfully with the more simple, undirected PLP potential (see the prior section on Empirical Scoring Functions) that emphasizes the general steric fit of receptor and ligand. Results are significantly worse when PLP is used for both docking and energy evaluation.
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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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Page 39 and 40: 10 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
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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 139 and 140: 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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