Reviews in Computational Chemistry Volume 18

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References 35 14. Daylight Chemical Information Systems Inc., 441 Greg Avenue, Santa Fe, NM 87501, USA. http://www.daylight.com. 15. MDL Information Systems, Inc., 14600 Catalina Street, San Leandro, CA 94577, USA. http:// www.mdl.com. 16. Accelrys (formerly Molecular Simulations Inc.), 9685 Scranton Road, San Diego, CA 92121- 3752, USA. http://www.accelrys.com. 17. Tripos Inc., 1699 South Hanley Road, St. Louis, MO 63144, USA. http://www.tripos.com. 18. W. A. Warr, in Computational Methods for the Analysis of Molecular Diversity, P. Willett, Ed., Perspectives in Drug Discovery and Design, Vol. 7/8, Kluwer/ESCOM, Dordrecht, The Netherlands, 1997, pp. 115–130. Commercial Software Systems for Diversity Analysis. 19. R. D. Brown, in Computational Methods for the Analysis of Molecular Diversity, P. Willett, Ed., Perspectives in Drug Discovery and Design, Vol. 7/8, Kluwer/ESCOM, Dordrecht, The Netherlands, 1997, pp. 31–49. Descriptors for Diversity Analysis. 20. R. D. Brown and Y. C. Martin, J. Chem. Inf. Comput. Sci., 36 (3), 572 (1996). Use of Structure–Activity Data to Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection. 21. R. D. Brown and Y. C. Martin, J. Chem. Inf. Comput. Sci., 37 (1), 1 (1997). The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding. 22. G. M. Downs and P. Willett, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., VCH Publishers, New York, 1995, Vol. 7, pp. 1–66. Similarity Searching in Databases of Chemical Structures. 23. P. Willett, J. M. Barnard, and G. M. Downs, J. Chem. Inf. Comput. Sci., 38 (6), 983 (1998). Chemical Similarity Searching. 24. J. M. Barnard, G. M. Downs, and P. Willett, in Virtual Screening for Bioactive Molecules, H.- J. Böhm and G. Schneider., Eds., Wiley, New York, 2000, pp. 59–80. Descriptor-Based Similarity Measures for Screening Chemical Databases. 25. J. S. Mason and S. D. Pickett, in Computational Methods for the Analysis of Molecular Diversity, P. Willett, Ed., Perspectives in Drug Discovery and Design, Vol. 7/8, Kluwer/ ESCOM, Dordrecht, The Netherlands, 1997, pp. 85–114. Partition-Based Selection. 26. J. A. Hartigan, Clustering Algorithms, Wiley, New York, 1975. 27. F. Murtagh, COMPSTAT Lectures, 4, (1985). Multidimensional Clustering Algorithms. 28. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, Prentice Hall, Englewood Cliff, NJ, 1988. 29. G. N. Lance and W. T. Williams, Computer J., 9, 373 (1967). A General Theory of Classificatory Sorting Strategies. 1. Hierarchical Systems. 30. J. H. Ward, J. Am. Stat. Assoc., 58, 236 (1963). Hierarchical Grouping to Optimize an Objective Function. 31. E. M. Voorhees, Inf. Processing Management, 22 (6), 465 (1986). Implementing Agglomerative Hierarchic Clustering Algorithms for Use in Document Retrieval. 32. X. Chen, A. Rusinko III, and S. S. Young, J. Chem. Inf. Comput. Sci., 38 (6), 1054 (1998). Recursive Partitioning Analysis of a Large Structure-Activity Data Set Using Three-Dimensional Descriptors. 33. A. Guenoche, P. Hansen, and B. Jaumard, J. Classification, 8, 5 (1991). Efficient Algorithms for Divisive Hierarchical Clustering with the Diameter Criterion. 34. R. A. Jarvis and E. A. Patrick, IEEE Trans. Computers, C-22 (11), 1025 (1973). Clustering Using a Similarity Measure Based on Shared Near Neighbors. 35. S. A. Perry and P. Willett, J. Inf. Sci., 6, 59 (1983). A Review of the Use of Inverted Files for Best Match Searching in Information Rretrieval Systems. 36. A. P. Dempster, N. M. Laird, and D. B. Rubin, J. Royal Stat. Soc., B39, 1 (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. 37. T. Kohonen, Self-organizing Maps, Springer-Verlag, Berlin, 1995.
36 Clustering Methods and Their Uses in Computational Chemistry 38. J. Zupan and J. Gasteiger, Neural Networks for Chemists, An Introduction, VCH, Weinheim, 1993. See also, K. L. Peterson, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York, 2000, Vol. 16, pp. 53–140. Artificial Neural Networks and Their Use in Chemistry. 39. F. Murtagh, in Handbook of Massive Data Sets, J. Abello, P. M. Pardalos, and M. G. C. Reisende, Eds., Kluwer, Dordrecht, The Netherlands, 2001, pp. 401–545. Clustering in Massive Data Sets. 40. D. W. Matula, in Classification as a Tool of Research, W. Gaul and M. Schader, Eds., Elsevier Science (North-Holland), Amsterdam, 1986, pp. 289–301. Divisive vs. Agglomerative Average Linkage Hierarchical Clustering. 41. N. C. Jain, A. Indrayan, and L. R. Goel, Pattern Recognition, 19 (1), 95 (1986). Monte Carlo Comparison of Six Hierarchical Clustering Methods on Random Data. 42. J. Podani, Vegetatio, 81, 61 (1989). New Combinatorial Clustering Methods. 43. M. Roux, in Applied Multivariate Analysis in SAR and Environmental Studies, J. Devillers and W. Karcher, Eds., Kluwer, Dordrecht, The Netherlands, 1991, pp. 115–135. Basic Procedures in Hierarchical Cluster Analysis. 44. A. El-Hamdouchi and P. Willett, Computer J., 32, 220 (1989). Hierarchic Document Clustering using Ward’s Method. 45. E. M. Rasmussen and P. Willett, J. Doc., 45 (1), 1 (1989). Efficiency of Hierarchical Agglomerative Clustering Using the ICL Distributed Array Processor. 46. G. M. Downs, P. Willett, and W. Fisanick, J. Chem. Inf. Comput. Sci., 34 (5), 1094 (1994). Similarity Searching and Clustering of Chemical-Structure Databases Using Molecular Property Data. 47. S. Guha, R. Rastogi, and K. Shim, Technical Report, Bell Laboratories, Murray Hill, NJ, 1997. A Clustering Algorithm for Categorical Attributes. 48. S. Guha, R. Rastogi, and K. Shim, Inf. Systems, 25 (5), 345 (2000). ROCK: A Robust Clustering Algorithm for Categorical Attributes. 49. S. Guha, R. Rastogi, and K. Shim, in Proceedings of the ACM SIGMOD International Conference on Management of Data, Seattle, WA, 1998, pp. 73–84. CURE: An Efficient Clustering Algorithm for Large Datasets. 50. G. Karypis, E.-H. Han, and V. Kumar, IEEE Computer: Special Issue on Data Analysis and Mining, 32 (8), 68 (1999). Chameleon: A Hierarchical Clustering Algorithm Using Dynamic Modeling. 51. G. Karypis, E.-H. Han, and V. Kumar, Technical Report No. 99-020, Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, 1999. Multilevel Refinement for Hierarchical Clustering. 52. D. Fasulo, Technical Report No. 01-03-02, Department of Computer Science & Engineering, University of Washington, Seattle, WA, 1999. An Analysis of Recent Work on Clustering Algorithms. 53. J. MacCuish, C. Nicolaou, and N. E. MacCuish, J. Chem. Inf. Comput. Sci., 41 (1), 134 (2001). Ties in Proximity and Clustering Compounds. 54. J. A. Garcia, J. Fdez-Valdivia, J. F. Cortijo, and R. Molina, Signal Processing, 44 (2), 181 (1994). A Dynamic Approach for Clustering Data. 55. Y. Wang, H. Yan, and C. Sriskandarajah, J. Classification, 13, 231 (1996). The Weighted Sum of Split and Diameter Clustering. 56. M. Steinbach, G. Karypis, and V. Kumar, Technical Report 00-034, Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, 2000. A Comparison of Document Clustering Techniques. 57. D. M. Hawkins, S. S. Young, and A. Rusinko, Quant. Struct.–Act. Relat., 16, 396 (1997). Analysis of a Large Structure–Activity Data Set Using Recursive Partitioning. 58. X. Chen, A. Rusinko, and S. S. Young, J. Chem. Inf. Comput. Sci., 38 (6), 1054 (1998). Recursive Partitioning Analysis of a Large Structure–Activity Data Set Using Three-Dimensional Descriptors.
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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
Page 13 and 14: Contents xv Electron Transfer in Po
Page 15 and 16: Contributors John M. Barnard, Barna
Page 17 and 18: 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 63: 34 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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86 The Use of Scoring Functions in
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CHAPTER 3 Potentials and Algorithms
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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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