Reviews in Computational Chemistry Volume 18

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OUTLOOK Outlook 75 The first scoring functions were published about 10 years ago. Since then, much experience has been gained in their application and in assessing their accuracy. Significant progress in the development of better functions has been made over the last few years, and it appears as if there now exist scoring functions that can be applied to a wide range of different proteins and which consistently yield considerable enrichment of active compounds. Consequently, many large and small pharmaceutical companies are increasingly using virtual screening techniques to identify possible leads. In fact, structure-based ligand design is now seen as a very important approach to drug discovery that nicely complements HTS. 217 High throughout screening has a number of serious disadvantages: it is expensive, 218 and it leads to many false positives and few real leads. 22,219 Furthermore, not all biactivity tests are amenable to HTS techniques. And finally, despite the large size of the chemical libraries available to the pharmaceutical industry, it is far from possible to cover the whole universe of drug-like organic molecules. Because of these limitations, and given the current aggressive patenting strategies, the focused design of novel compounds and compound libraries will continue to gain importance. Thus, there is every reason to believe that the value of structure-based approaches will continue to grow and become even more embraced by the pharmaceutical, agricultural, and related industries than it now is. The development of improved scoring functions is certainly vital for their success. The major challenges to be overcome in the further development of scoring functions include 1. Polar interactions are still not treated adequately. It is somewhat strange to find that while the role of hydrogen bonds in biology has been well known for a long time and hydrogen bonds are qualitatively well understood, a quantitative treatment of hydrogen bonds in protein–ligand interactions is still missing. Therefore, hydrogen bonds have been referred to as ‘‘the last mystery in structure-based design.’’ 38 2. All scoring functions are essentially simple analytical functions fitted to experimental binding data. Presently, there exists a heavy bias in the public domain data toward peptidic ligands, which in turn leads to an overestimation of polar interactions in many scoring functions. The development of better scoring function clearly requires access to more data on nonpeptidic, low molecular weight, drug-like ligands. 3. Unfavorable interactions and unlikely docking solutions are not penalized strongly enough. General and robust methods that account for undesired features of complex structures in the derivation of scoring functions are still lacking.
76 The Use of Scoring Functions in Drug Discovery Applications 4. So far, fast scoring functions only cover part of the whole receptor–ligand binding process. A more detailed picture could be obtained by taking into account properties of the unbound ligand, that is, solvation effects and energetic differences between the low-energy solution conformations and the bound conformation. ACKNOWLEDGMENTS The authors have benefited from numerous discussions with many researchers active in the field of docking and scoring. We especially thank Holger Gohlke (Marburg), Ingo Muegge (Bayer, U.S.A.), and Matthias Rarey (GMD, St. Augustin). Wolfgang Guba’s valuable comments on the manuscript are gratefully acknowledged. REFERENCES 1. J. Greer, J. W. Erickson, and J. J. Baldwin, J. Med. Chem., 37, 1035 (1994). Application of the Three-Dimensional Structures of Protein Target Molecules in Structure-Based Drug Design. 2. M. von Itzstein, W.-Y. Wu, G. B. Kok, M. S. Pegg, J. C. Dyason, B. Jin, T. V. Phan, M. L. Smythe, H. F. White, S. W. Oliver, P. M. Colmant, J. N. Varghese, D. M. Ryan, J. M. Woods, R. C. Bethell, V. J. Hotham, J. M. Cameron, and C. R. Penn, Nature (London), 363, 418 (1993). Rational Design of Potent Sialidase-Based Inhibitors of Influenza Virus Replication. 3. W. Lew, X. Chen, and U. Choung, Curr. Med. Chem., 7, 663 (2000). Discovery and Development of GS 4104 (Oseltamivir): An Orally Active Influenza Neuraminidase Inhibitor. 4. S. W. Kaldor, V. J. Kalish, J. F. Davies, V. S. Bhasker, J. E. Fritz, K. Appelt, J. A. Burgess, K. M. Campanale, N. Y. Chirgadze, D. K. Clawson, B. A. Dressman, S. D. Hatch, D. A. Khalil, M. B. Kosa, P. P. Lubbehusen, M. A. Muesing, A. K. Patick, S. H. Reich, K. S. Su, and J. H. Tatlock, J. Med. Chem., 40, 3979 (1997). Viracept (Nelfinavir Mesylate, AG1343): A Potent, Orally Bioavailable Inhibitor of HIV-1 Protease. 5. R. S. Bohacek, C. McMartin, and W. C. Guida, Med. Res. Rev., 16, 3 (1996). The Art and Practice of Structure-Based Drug Design: A Molecular Modeling Perspective. 6. R. E. Babine and S. L. Bender, Chem. Rev., 97, 1359 (1997). Molecular Recognition of Protein–Ligand Complexes: Applications to Drug Design. 7. R. E. Hubbard, Curr. Opin. Biotechnology, 8, 696 (1997). Can Drugs Be Designed? 8. D. B. Boyd, in Rational Molecular Design in Drug Research, Proceedings of the Alfred Benzon Symposium No. 42 (Copenhagen, June 8–12, 1997), T. Liljefors, F. S. Jørgensen, and P. Krogsgaard-Larsen, Eds., Munksgaard, Copenhagen, 1998, pp. 15–23. Progress in Rational Design of Therapeutically Interesting Compounds. 9. M. A. Murcko, P. R. Caron, and P. S. Charifson, Annu. Reports Med. Chem., 34, 297 (1999). Structure-Based Drug Design. 10. D. A. Gschwend, W. Sirawaraporn, D. V. Santi, and I. D. Kuntz, Proteins: Struct., Funct., Genet., 29, 59 (1997). Specificity in Structure-Based Drug Design: Identification of a Novel, Selective Inhibitor of Pneumocystis carinii Dihydrofolate Reductase. 11. E. K. Kick, D. C. Roe, A. G. Skillman, G. Liu, T. J. A. Ewing, Y. Sun, I. D. Kuntz, and J. A. Ellman, Chem. Biol., 4, 297 (1997). Structure-Based Design and Combinatorial Chemistry Yield Low Nanomolar Inhibitors of Cathepsin D.
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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 117 and 118: CHAPTER 3 Potentials and Algorithms
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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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