Reviews in Computational Chemistry Volume 18

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Application of Scoring Functions in Virtual Screening 65 also teach how to recognize typical failure cases. Recent examples of library ranking experiments include those by Charifson et al., 198 Bissantz, Folkers, and Rognan, 77 and Stahl and Rarey. 78 Charifson and co-workers compiled sets of several hundred active molecules for three different targets: p38 MAP kinase, inosine monophosphate dehydrogenase, and HIV protease. The members of these sets were then docked into the corresponding active sites together with 10,000 randomly chosen, but drug-like, commercial compounds using DOCK 98 and the Vertex in-house docking tool Gambler. Three scoring functions performed consistently well in enriching active compounds, namely, ChemScore, 71,199 the DOCK AMBER force field score, and PLP. 62 The finding that these three scoring functions performed so well was partially attributed to the fact that a rigid-body optimization could be carried out with these functions, because the functions include repulsive terms in contrast to many of the other tested functions. The study by Stahl and Rarey 78 compared the performance of DrugScore 92 and PMF 86 to that of PLP 62 and FlexX score using the docking program FlexX. 64–66 Interestingly, the two knowledgebased scoring functions showed significantly different behavior for extreme cases of active sites. DrugScore coped well with situations where ligands are tightly bound in narrow lipophilic cavities (e.g., COX-2 and the thrombin S1 pocket), whereas PMF did not lead to good enrichment in such cases. Conversely, for the very polar binding site of neuraminidase, PMF gave better enrichment than any other scoring function, whereas DrugScore failed. The description of complexes in which many hydrogen bonds play a role seems to be a general strength of PMF. This has also been noted by Bissantz, Folkers and Rognan, 77 who found PMF to perform well for the polar target thymidine kinase and less well for the estrogen receptor. Hydrogen Bonding versus Hydrophobic Interactions It is of central importance in virtual screening to achieve a balanced description of hydrogen bonding and hydrophobic contributions to the score in order to avoid a bias toward either highly polar or completely hydrophobic molecules. Empirical scoring functions have the advantage that they can be quickly reparameterized to achieve such a balance, whereas such an adjustment is impossible with knowledge-based functions. Because this is such an important topic, we will illuminate it with a number of examples. Consider the following database ranking experiment. A database of about 7600 compounds was flexibly docked into the ATP binding site of p38 MAP kinase. The database consisted of ca. 7500 random compounds from the World Drug Index (WDI) 200 and 72 inhibitors of p38 MAP kinase, which in turn consisted of 30 inhibitors forming two hydrogen bonds with the receptor and 20 inhibitors forming only one. Both groups covered the same activity range from low micromolar (mM) to about 10 nM. For each of the docked compounds, up to 800 alternative docking solutions were
66 The Use of Scoring Functions in Drug Discovery Applications Ranks of known inhibitors 2H-bonds 1H-bond 2H-bonds 1H-bond PLP score FlexX score Figure 4 Results of a seeding experiment. The ranks of known p38 MAP kinase inhibitors are shown as horizontal lines in the four diagrams. Inhibitors have been divided into two classes: those forming one or two hydrogen bonds to the p38 MAP kinase ATP binding site. The FlexX scoring function preferentially enriches those inhibitors that form two hydrogen bonds. This tendency is less pronounced for the PLP scoring function. The inhibitors with the best predicted affinities are at the top. Data is shown for the top 300 compounds in terms of docking scores. generated by FlexX 64–66 using the FlexX scoring function. These alternative solutions were rescored separately by the FlexX and PLP 62 scoring functions to select the lowest energy docking solution per compound. The compounds in the database were then ranked according to these scores. Figure 4 shows the ranks of the known inhibitors among the top 350 compounds as calculated by both scoring functions. Although the overall performance of both scoring functions in enriching inhibitors is comparable, it is obvious that the FlexX score ‘‘specializes’’ on the doubly hydrogen-bonded inhibitors. On the other hand, if one were to select screening candidates from the PLP list, one would most likely select both types of inhibitors. The PLP function generally emphasizes steric complementarity and hydrophobic interactions with its more far-reaching pair potential, whereas the FlexX score emphasizes hydrogen-bond complementarity. A combination of PLP and FlexX scoring functions called ScreenScore was published recently. 78 It was derived by performing a systematic optimization of library ranking results over seven targets, whose receptor sites cover a wide range of form, size, and polarity. ScreenScore was designed to be a robust and general scoring function that combines the virtues of both PLP and FlexX. Figure 5 shows that this is indeed the case. ScreenScore gives good enrichment values for cyclooxygenase-2 (COX-2 has a highly lipophilic binding site), and neuraminidase (which has a highly polar site), whereas the individual functions fail in one of the two cases. The authors of PLP have recently enhanced their 0 100 200 300
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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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12 Clustering Methods and Their Use
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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
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Page 139 and 140: where we have used q Cl ¼ qNa. The
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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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