Computer-aided molecular diversity analysis and combinatorial library design

Reviews in Computational Chemistry

Volumn 16, Issue , 2000, Pages 1-51

  Lewis, Richard A ^a   Pickett, Stephen D ^b,c   Clark, David E ^b  

^a ELI LILLY AND COMPANY   (United States)

^b Aventis Pharma Ltd   (United Kingdom)

^c ROCHE PRODUCTS LTD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0034355922     PISSN: 10693599     EISSN: None     Source Type: Book Series    
DOI: None     Document Type: Article

References (249)

1. B. Merrifield, J. Am. Chem. Soc., 85, 2149 (1963). Solid Phase Peptide Synthesis. I. The Synthesis of a Tetrapeptide.
2. C. Desai, R. N. Zuckermann, and W. H. Moos, Drug Dev. Res., 33, 174 (1994). Recent Advances in the Generation of Chemical Diversity Libraries.
3. M. Geysen, S. Barteling, and R. Moelen, Proc. Natl. Acad. Sci. USA, 81, 3998 (1984). Use of Peptide Synthesis to Probe Viral Antigens for Epitopes to a Resolution of a Single Amino Acid.
4. R. A. Houghten, Proc. Natl. Acad. Sci. USA, 82, 5131 (1985). General Method for the Rapid Solid-Phase Synthesis of Large Numbers of Peptides: Specificity of Antigen-Antibody Interaction at the Level of Individual Amino Acids.
5. K. S. Lam, S. E. Salmon, E. M. Hersh, V. J. Hruby, W. M. Kazmierski, and R. J. Knapp, Nature, 354, 82 (1991). A New Type of Synthetic Peptide Library for Identifying Ligand-Binding Activity.
6. L. A. Thompson and J. A. Ellman, Chem. Rev., 96, 555 (1996). Synthesis and Applications of Small Molecule Libraries.
7. E. M. Gordon, M. A. Gallop, and D. V. Patel, Acc. Chem. Res., 29, 144 (1996). Strategy and Tactics in Combinatorial Organic Synthesis. Applications to Drug Discovery.
8. F. Balkenhohl, C. von dem Bussche-Huennefeld, A. Lansky, and C. Zechel, Angew. Chem. Int. Ed. Engl., 35, 2288 (1996). Combinatorial Synthesis of Small Organic Molecules.
9. E. R. Felder and D. Poppinger, Adv. Drug Res., 30, 111 (1997). Combinatorial Compound Libraries for Enhanced Drug Discovery Approaches.
10. D. Brown, Mol. Diversity, 2, 217 (1997). Future Pathways for Combinatorial Chemistry.
11. P. L. Myers, Curr. Opin. Biotechnol., 8, 701 (1997). Will Combinatorial Chemistry Deliver Real Medicines?
12. R. E. Dolle, Mol. Diversity, 3, 199 (1998). Comprehensive Survey of Chemical Libraries Yielding Enzyme Inhibitors, Receptor Agonists and Antagonists, and Other Biologically Active Agents: 1992 Through 1997.
13. J.-L. Fauchere, J. A. Boutin, J.-M. Henlin, N. Kucharczyk, and J.-C. Ortuno, Chemom. Intell. Lab. Syst., 43 (1, 2), 43 (1998). Combinatorial Chemistry for the Generation of Molecular Diversity and the Discovery of Bioactive Leads.
14. J. M. Blaney and E. J. Martin, Curr. Opin. Chem. Biol., 1, 54 (1997). Computational Approaches for Combinatorial Library Design and Molecular Diversity Analysis.
15. E. J. Martin, D. C. Spellmeyer, R. E. Critchlow Jr., and J. M. Blaney, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., VCH Publishers, New York, 1997, Vol. 10, pp. 75-100. Does Combinatorial Chemistry Obviate Computer-Aided Drug Design?
16. M. G. Bures and Y. C. Martin, Curr. Opin. Chem. Biol., 2, 376 (1998). Computational Methods in Molecular Diversity and Combinatorial Chemistry.
17. D. K. Agrafiotis, J. C. Myslik, and F. R. Salemme, Mol. Diversity, 4, 1 (1999). Advances in Diversity Profiling and Combinatorial Series Design.
18. P. Willett, Perspect. Drug Discovery Des., 7/8, 1 (1997). Computational Tools for the Analysis of Molecular Diversity.
19. For more recent material, see: D. K. Agrafiotis and E. J. Martin, J. Mol. Graphics Modell., 18, (3/4), in press (2000). Combinatorial Library Design.
20. H. Kubinyi, Perspect. Drug Discovery Des., 9/10/11, 225 (1998). Similarity and Dissimilarity: A Medicinal Chemist's View.
21. G. Sello, J. Chem. Inf. Comput. Sci., 38, 691 (1998). Similarity Measures: Is It Possible to Compare Dissimilar Structures?
22. World Drug Index. Derwent Information, http://www.derwent.com/.
23. E. J. Martin, R. E. Critchlow Jr., D. C. Spellmeyer, S. Rosenberg, K. L. Spear, and J. M. Blaney, Pharmacochem. Libr., 29, 133 (1998). Diverse Approaches to Combinatorial Library Design.
24. R. S. Bohacek, C. McMartin, and W. C. Guida, Med. Res. Rev., 16, 3 (1996). The Art and Practice of Structure-Based Drug Design.
25. H. Kubinyi, Curr. Opin. Drug Discovery Dev., 1, 4 (1998). Structure-Based Design of Enzyme Inhibitors and Receptor Ligands.
26. P. M. Dean, Molecular Foundations of Drug-Receptor Interaction, Cambridge University Press, Cambridge, 1987.
27. W. P. Jencks, in Chemical Recognition in Biology, F. Chapeville and A.-L. Haenni, Eds., Springer-Verlag, Berlin, 1980, pp. 3-25. What Everyone Wanted to Know About Tight Binding and Catalysis, But Never Thought of Asking.
28. H.-J. Böhm and G. Klebe, Angew. Chem. Int. Ed. Engl., 35, 2588 (1996). What Can We Learn from Molecular Recognition in Protein-Ligand Complexes for the Design of New Drugs?
29. R. L. Babine and S. L. Bender, Chem. Rev., 97, 1359 (1997). Molecular Recognition of Protein-Ligand Complexes: Application to Drug Design.
30. G. Klebe and H.-J. Böhm, J. Recept. Signal. Transduction Res., 17, 459 (1997). Energetic and Entropic Factors Determining Binding Affinity in Protein-Ligand Complexes.
31. D. H. Williams, Chem. Soc. Rev., 28, 57 (1998). Aspects of Weak Interactions.
32. J. R. H. Tame, J. Comput.-Aided Mol. Des., 13, 99 (1999). Scoring Functions: A View from the Bench.
33. A. R. Fersht, J.-P. Shi, J. Knill-Jones, D. M. Lowe, A. J. Wilkinson, D. M. Blow, P. Brick, P. Carter, M. M. Y. Waye, and G. Winter, Nature, 314, 235 (1985). Hydrogen Bonding and Biological Specificity Analyzed by Protein Engineering.
34. A. Horovitz, L. Serrano, B. Avron, M. Bycroft, and A. R. Fersht, J. Mol. Biol., 216, 1031 (1990). Strength and Cooperativity of Contributions of Surface Salt Bridges to Protein Stability.
35. A. J. Doig and D. H. Williams, J. Am. Chem. Soc., 114, 338 (1992). Binding Energy of an Amide-Amide Hydrogen Bond in Aqueous and Nonpolar Solvents.
36. P. L. Chau and P. M. Dean, J. Comput.-Aided Mol. Des., 8, 513 (1994). Electrostatic Complementarity Between Proteins and Ligands. 1. Charge Disposition, Dielectric and Interface Effects.
37. P. L. Chau and P. M. Dean, J. Comput.-Aided Mol. Des., 8, 527 (1994). Electrostatic Complementarity Between Proteins and Ligands. 2. Ligand Moieties.
38. P. L. Chau and P. M. Dean, J. Comput.-Aided Mol. Des., 8, 545 (1994). Electrostatic Complementarity Between Proteins and Ligands. 3. Structural Basis.
39. D. Eisenberg and A. D. McLachlan, Nature, 319, 199 (1986). Solvation Energy in Protein Folding and Binding.
40. A. Ben-Naim, Hydrophobic Interactions, Plenum Press, New York, 1980.
41. D. G. Alberg and S. L. Schreiber, Science, 262, 248 (1993). Structure-Based Design of a Cyclophilin-Calcineurin Bridging Ligand.
42. A. R. Khan, J. C. Parrish, M. E. Fraser, W. W. Smith, P. A. Bartlett, and M. N. G. James, Biochemistry, 37, 16839 (1998). Lowering of the Entropic Barrier for Binding Conformationally Flexible Inhibitors to Enzymes.
43. B. J. Stockman, Prog. Nucl. Magn. Reson. Spectrosc., 33, 109 (1998). NMR Spectroscopy as a Tool for Structure-Based Drug Design.
44. 15N NMR Relaxation Studies of Free and Inhibitor-Bound 4-Oxalocrotonate Tautomerase: Backbone Dynamics and Entropy Changes of an Enzyme upon Inhibitor Binding.
45. L. K. Nicholson, T. Yamazaki, D. A. Torchia, S. Grzesiek, A. Bax, S. J. Stahl, J. D. Kaufman, P. T. Wingfield, P. Y. S. Lam, P. K. Jadhav, C. N. Hodge, P. J. Domaille, and C.-H. Chang, Nat. Struct. Biol., 2, 274 (1995). Flexibility and Function in HIV-1 Protease.
46. X. Leng, S. Y. Tsai, B. W. O'Malley, and M. J. Tsai, J. Steroid Biochem. Mol. Biol., 46, 643 (1993). Ligand-Dependent Conformational Changes in Thyroid Hormone and Retinoic Acid Receptors Are Potentially Enhanced by Heterodimerization with Retinoic X Receptor.
47. A. M. Davis and S. J. Teague, Angew. Chem. Int. Ed. Engl., 38, 736 (1999). Hydrogen Bonding, Hydrophobic Interactions, and Failure of the Rigid Receptor Hypothesis.
48. A. Wlodawer and J. Vondrasek, Annu. Rev. Biophys. Biomol. Struct., 27, 249 (1998). Inhibitors of HIV-1 Protease: A Major Success of Structure-Assisted Drug Design.
49. A. R. Leach, J. Mol. Biol., 235, 345 (1994). Ligand Docking to Proteins with Discrete Sidechain Flexibility.
50. G. Jones, P. Willett, and R. C. Glen, J. Mol. Biol., 245, 43 (1995). Molecular Recognition of Receptor Sites Using a Genetic Algorithm with a Description of Desolvation.
51. V. Schnecke, C. A. Swanson, E. D. Getzoff, J. A. Tainer, and L. A. Kuhn, Proteins: Struct., Funct., Genet., 33, 74 (1998). Screening a Peptidyl Database for Potential Ligands to Proteins with Side-Chain Flexibility.
52. B. Sandak, R. Nussinov, and H. J. Wolfson, J. Comput. Biol., 5, 631 (1998). A Method for Biomolecular Structural Recognition and Docking Allowing Conformational Flexibility.
53. F. A. Quiocho, D. K. Wilson, and N. K. Vyas, Nature, 340, 404 (1989). Substrate Specificity and Affinity of a Protein Modulated by Bound Water Molecules.
54. M. L. Raymer, P. C. Sanschagrin, W. F. Punch, S. Venkataraman, E. D. Goodman, and L. A. Kuhn, J. Mol. Biol., 265, 445 (1997). Predicting Conserved Water-Mediated and Polar Ligand Interactions in Proteins Using a K-Nearest-Neighbors Genetic Algorithm.
55. V. A. Makarov, B. K. Andrews, and B. M. Pettitt, Biopolymers, 45, 469 (1998). Reconstructing the Protein-Water Interface.
56. M. Feig and B. M. Pettitt, Structure, 6, 1351 (1998). Crystallographic Water Sites from a Theoretical Perspective.
57. M. Rarey, B. Kramer, T. Lengauer, and G. Klebe, J. Mol. Biol., 261, 470 (1996). A Fast Flexible Docking Method Using an Incremental Construction Algorithm.
58. M. Rarey, B. Kramer, and T. Lengauer, Proteins: Struct., Func., Genet., 34, 17 (1999). The Particle Concept: Placing Discrete Water Molecules During Protein-Ligand Docking Predictions.
59. E. F. Meyer, I. Botos, L. Scapozza, and D. Zhang, Perspect. Drug Discovery Des., 3, 168 (1995). Backward Binding and Other Structural Surprises.
60. G. D. Diana, A. M. Treasurywala, T. R. Bailey, R. C. Oglesby, D. C. Pevear, and F. J. Dutko, J. Med. Chem., 33, 1306 (1990). A Model for Compounds Active Against Human Rhinovirus-14 Based on X-Ray Crystallography Data.
61. R. D. Brown, Perspect. Drug Discovery Des., 7/8, 31 (1997). Descriptors for Diversity Analysis.
62. R. S. Pearlman, Chem. Des. Autom. News, 2 (1), 1 (1987). Rapid Generation of High Quality Approximate 3D Molecular Structures.
63. J. Sadowski and J. Gasteiger, Chem. Rev., 93, 2567 (1993). From Atoms and Bonds to Three-Dimensional Atomic Coordinates.
64. N. E. Shemetulskis, D. Weininger, C. J. Blankley, J. J. Yang, and C. Humblet, J. Chem. Inf. Comput. Sci., 36, 862 (1996). Stigmata: An Algorithm to Determine Structural Commonalities in Diverse Datasets.
65. P. Willett, V. Winterman, and D. Bawden, J. Chem. Inf. Comput. Sci., 26, 109 (1986). Implementation of Nonhierarchical Cluster Analysis Methods in Chemical Information Systems: Selection of Compounds for Biological Testing and Clustering of Substructure Search Output.
66. SSKEYS Gateway, MDL Information Systems Inc., 14600 Catalina St., San Leandro, CA 94577. http.//www.mdli.com/.
67. R. D. Brown and Y. C. Martin, J. Chem. Inf. Comput. Sci., 36, 572 (1996). Use of Structure-Activity Data to Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection.
68. M. J. McGregor and P. V. Pallai, J. Chem. Inf. Comput. Sci., 37, 443 (1997). Clustering of Large Databases of Compounds: Using the MDL Keys as Structural Descriptors.
69. R. D. Brown and Y. C. Martin, J. Chem. Inf. Comput. Sci., 37, 1 (1997). The Information Content of 2-D and 3-D Structural Descriptors Relevant to Ligand-Receptor Binding.
70. Daylight Chemical Information Software, version 4.62. Daylight Chemical Information Systems Inc., 27401 Los Altos, Suite 370, Mission Viejo, CA 92691. http:// www.daylight.com/.
71. D. R. Flower, J. Chem. Inf. Comput. Sci., 38, 379 (1998). On the Properties of Bit String-Based Measures of Chemical Similarity.
72. P. Willett, J. M. Barnard, and G. M. Downs, J. Chem. Inf. Comput. Sci., 38, 983 (1998). Chemical Similarity Searching.
73. L. H. Hall and L. B. Kier, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., VCH Publishers, New York, 1991, Vol. 2, pp. 367-422. The Molecular Connectivity Chi Indexes and Kappa Shape Indexes in Structure-Property Modeling.
74. A. T. Balaban, SAR QSAR Environ. Res., 8, 1 (1998). Topological and Stereochemical Molecular Descriptors for Databases Useful in QSAR Similarity/Dissimilarity and Drug Design.
75. R. A. Lewis, J. S. Mason, and I. M. McLay, J. Chem. Inf. Comput. Sci., 37, 599 (1997). Similarity Measures for Rational Set Selection and Analysis of Combinatorial Libraries: The Diverse Property-Derived (DPD) Approach.
76. E. J. Martin, J. M. Blaney, M. A. Siani, D. C. Spellmeyer, A. K. Wong, and W. H. Moos, J. Med. Chem., 38, 1431 (1995). Measuring Diversity: Experimental Design of Combinatorial Libraries for Drug Discovery.
77. D. J. Cummins, C. W. Andrews, J. A. Bentley, and M. Cory, J. Chem. Inf. Comput. Sci., 36, 750 (1996). Molecular Diversity in Chemical Databases: Comparison of Medicinal Chemistry Knowledge Bases and Databases of Commercially Available Compounds.
78. S. Wold, K. Esbensen, and P. Geladi, Chemom. Intell. Lab. Syst., 2, 37 (1987). Principal Component Analysis.
79. B. S. Everitt and G. Dunn, Applied Multivariate Data Analysis, Oxford University Press, New York, 1992.
80. W. S. Dillon and M. Goldstein, Multivariate Analysis: Methods and Applications, Wiley, New York, 1984.
81. CLOGP. Daylight Chemical Information Systems Inc., 27401 Los Altos, Suite 370, Mission Viejo, CA 92691. http://www.daylight.com/; see also http://biobyte.com/.
82. A. J. Leo, Chem. Rev., 93, 1281 (1993). Calculating log Poct from Structures.
83. P.-A. Carrupt, B. Testa, and P. Gaillard, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York, 1997, Vol. 11, pp. 241-315. Computational Approaches to Lipophilicity: Methods and Applications.
84. P. F. de Aguiar, B. Bourguignon, M. S. Khots, D. L. Massart, and R. Phan-Than-Luu, Chemom. Intell. Lab. Syst., 30, 199 (1992). D-Optimal Designs.
85. R. S. Pearlman and K. M. Smith, Perspect. Drug Discovery Des., 9/10/11, 355 (1997). Novel Software Tools for Chemical Diversity.
86. R. S. Pearlman and K. M. Smith, Drugs Future, 23, 885 (1998). Software for Chemical Diversity in the Context of Accelerated Drug Discovery.
87. F. R. Burden, J. Chem. Inf. Comput. Sci., 29, 225 (1989). Molecular Identification Number for Substructure Searches.
88. P. R. Menard, J. S. Mason, I. Morize, and S. Bauerschmidt, J. Chem. Inf. Comput. Sci., 38, 1204 (1998). Chemistry Space Metrics in Diversity Analysis, Library Design, and Compound Selection.
89. R. S. Pearlman and K. M. Smith, J. Chem. Inf. Comput. Sci., 39, 28 (1999). Metric Validation and the Receptor-Relevant Subspace Concept.
90. D. Stanton, J. Chem. Inf. Comput. Sci., 39, 11 (1999). Evaluation and Use of BCUT Descriptors in QSAR and QSPR Studies.
91. G. W. Bemis and I. D. Kuntz, J. Compnt.-Aided Mol. Des., 6, 607 (1992). A Fast and Efficient Method for 2D and 3D Molecular Shape Description.
92. G. Moreau and C. Turpin, Analusis, 24, 17 (1996). Use of Similarity Analysis to Reduce Large Molecular Libraries to Smaller Sets of Representative Molecules.
93. J. Sadowski, M. Wagener, and J. Gasteiger, Angew. Chem. Int. Ed. Engl., 34, 2674 (1996). Assessing Similarity and Diversity of Combinatorial Libraries by Spatial Autocorrelation Functions and Neural Networks.
94. S. E. Jakes and P. Willett, J. Mol. Graphics, 4, 12 (1986). Pharmacophoric Pattern Matching in Files of 3-D Chemical Structures: Selection of Interatomic Distance Screens.
95. S. E. Jakes, N. Watts, P. Willett, D. Bawden, and J. D. Fisher, J. Mol. Graphics, 5, 41 (1987). Pharmacophoric Pattern Matching in Files of 3-D Chemical Structures: Evaluation of Search Performance.
96. R. P. Sheridan, R. Nilakantan, A. Rusinko III, N. Bauman, K. S. Haraki, and R. Venkataraghavan, J. Chem. Inf. Comput. Sci., 29, 255 (1989). 3-DSEARCH: A System for Three-Dimensional Substructure Searching.
97. Y. C. Martin, M. G. Bures, and P. Willett, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., VCH Publishers, New York, 1990, Vol. 1, pp. 213-263. Searching Databases of Three-Dimensional Structures.
98. Y. C. Martin, J. Med. Chem., 35, 2145 (1992). 3-D Database Searching in Drug Design.
99. A. C. Good and J. S. Mason, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., VCH Publishers, New York, 1995, Vol. 7, pp. 67-117. Three-Dimensional Structure Database Searches.
100. S. Wang, G. W. A. Milne, X. Yan, I. Posey, M. C. Nicklaus, L. Graham, and W. G. Rice, J. Med. Chem., 39, 2047 (1996). Discovery of Novel, Non-Peptide HIV-1 Protease Inhibitors by Pharmacophore Searching.
101. P. C. Astles, T. J. Brown, C. M. Handscombe, M. F. Harper, N. V. Harris, R. A. Lewis, P. M. Lockey, C. McCarthy, I. M. McLay, B. Porter, A. G. Roach, C. Smith, and R. J. A. Walsh, Eur. J. Med. Chem., 32, 409 (1997). Selective Endothelin A Receptor Ligands. 1. Discovery and Structure-Activity of 2,4-Disubstituted Benzoic Acid Derivatives.
102. S. D. Pickett, J. S. Mason, and I. M. McLay, J. Chem. Inf. Comput. Sci., 36, 1214 (1996). Diversity Profiling and Design Using 3-D Pharmacophores: Pharmacophore-Derived Queries (PDQ).
103. J. S. Mason and S. D. Pickett, Perspect. Drug Discovery Des., 7/8, 85 (1997). Partition-Based Selection.
104. S. D. Pickett, C. Luttmann, V. Guerin, A. Laoui, and E. James, J. Chem. Inf. Comput. Sci., 38, 144 (1998). DIVSEL and COMPLIB - Strategies for the Design and Comparison of Combinatorial Libraries Using Pharmacophoric Descriptors.
105. E. K. Davies, in Molecular Diversity and Combinatorial Chemistry: Libraries and Drug Discovery, I. M. Chaiken and K. D. Janda, Eds., American Chemical Society, Washington, DC, 1996, pp. 309-316. Using Pharmacophore Diversity to Select Molecules to Test from Commercial Catalogues.
106. R. D. Brown and Y. C. Martin, J. Med. Chem., 40, 2304 (1997). Designing Combinatorial Library Mixtures Using a Genetic Algorithm.
107. ChemDiverse. Oxford Molecular Group plc, The Medawar Centre, Oxford Science Park, Oxford, OX4 4GA, United Kingdom. http://www.oxmol.com/.
108. R. D. Cramer, R. D. Clark, D. E. Patterson, and A. M. Ferguson, J. Med. Chem., 39, 3060 (1996). Bioisosterism as a Molecular Diversity Descriptor: Steric Fields of Single Topomeric Conformers.
109. J. Mount, J. Ruppert, W. Welch, and A. N. Jain, J. Med. Chem., 42, 60 (1999). IcePick: A Flexible Surface-Based System for Molecular Diversity.
110. W. Welch, J. Ruppert, and A. N. Jain, Chem. Biol., 3, 449 (1996). Hammerhead: Fast, Fully Automated Docking of Flexible Ligands to Protein Binding Sites.
111. A. N. Jain, K. Koile, and D. Chapman, J. Med. Chem., 37, 2315 (1994). Compass: Predicting Biological Activities from Molecular Surface Properties. Performance Comparisons on a Steroid Benchmark.
112. S. M. Boyd, M. Beverley, L. Norskov, and R. E. Hubbard, J. Comput.-Aided Mol. Des., 9, 417 (1995). Characterising the Geometric Diversity of Functional Groups in Chemical Databases.
113. P. A. Bartlett and G. Lauri, in Book of Abstracts, 211th ACS National Meeting, New Orleans, LA, March 24-28, 1996, American Chemical Society, Washington, DC, 1996, COMP-014. The CAVEAT Vector Approach for Structure-Based Design and Combinatorial Chemistry.
114. D. Chapman, J. Comput.-Aided Mol. Des., 10, 501 (1996). The Measurement of Molecular Diversity: A Three-Dimensional Approach.
115. G. Jones, P. Willett, R. C. Glen, A. R. Leach, and R. Taylor, J. Mol. Biol., 267, 727 (1997). Development and Validation of a Genetic Algorithm for Flexible Docking.
116. C. A. Baxter, C. W. Murray, D. E. Clark, D. R. Westhead, and M. D. Eldridge, Proteins: Struct., Funct., Genet., 33, 367 (1998). Flexible Docking Using Tabu Search and an Empirical Estimate of Binding Affinity.
117. C. W. Murray, D. E. Clark, T. R. Auton, M. A. Firth, J. Li, R. A. Sykes, B. Waszkowycz, D. R. Westhead, and S. C. Young, J. Comput.-Aided Mol. Des., 11, 193 (1997). PRO_SELECT: Combining Structure-Based Drug Design and Combinatorial Chemistry for Rapid Lead Discovery. 1. Technology.
118. D. E. Clark, D. Frenkel, S. A. Levy, J. Li, C. W. Murray, B. Robson, B. Waszkowycz, and D. R. Westhead, J. Comput.-Aided Mol. Des., 9, 13 (1995). PRO_LIGAND: An Approach to De Novo Molecular Design. 1. Application to the Design of Organic Molecules.
119. M. D. Eldridge, C. W. Murray, T. R. Auton, G. V. Paolini, and R. P. Mee, J. Comput.-Aided Mol. Des., 11, 425 (1997). Empirical Scoring Functions. I. The Development of a Fast Empirical Scoring Function to Estimate the Binding Affinity of Ligands in Receptor Complexes.
120. 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.
121. T. S. Haque, A. G. Skillman, C. E. Lee, H. Habashita, I. Y. Gluzman, T. J. A. Ewing, D. E. Goldberg, I. D. Kuntz, and J. A. Ellman, J. Med. Chem., 42, 1428 (1999). Potent, Low-Molecular-Weight Non-Peptide Inhibitors of Malarial Aspartyl Protease Plasmepsin II.
122. Y. Sun, T. J. A. Ewing, A. G. Skillman, and I. D. Kuntz, J. Comput.-Aided Mol. Des., 12, 597 (1998). CombiDOCK: Structure-Based Combinatorial Docking and Library Design.
123. H.-J. Böhm, J. Comput.-Aided Mol. Des., 6, 61 (1992). The Computer Program LUDI: A New Method for the De Novo Design of Enzyme Inhibitors.
124. H.-J. Böhm, J. Comput.-Aided Mol. Des., 10, 265 (1996). Towards the Automatic Design of Synthetically Accessible Protein Ligands: Peptides, Amides and Peptidomimetics.
125. H.-J. Böhm, D. W. Banner, and L. Weber, J. Comput.-Aided Mol. Des., 13, 51 (1999). Combinatorial Docking and Combinatorial Chemistry: Design of Potent Non-peptide Thrombin Inhibitors.
126. Design in Receptor. Oxford Molecular Group plc, The Medawar Centre, Oxford Science Park, Oxford, OX4 4GA, United Kingdom. http://www.oxmol.co.uk/.
127. C. M. Murray and S. J. Cato, J. Chem. Inf. Comput. Sci., 39, 46 (1999). Design of Libraries to Explore Receptor Sites.
128. M. Lajiness, in QSAR: Rational Approaches to the Design of Bioactive Compounds, C. Silipo and A. Vittoria, Eds., ESCOM, Leiden, 1991, pp. 201-204. Evaluation of the Performance of Dissimilarity Selection Methodology.
129. R. Taylor, J. Chem. Inf. Comput. Sci., 35, 59 (1995). Simulation Analysis of Experimental Design Strategies for Screening Random Compounds as Potential New Drugs and Agrochemicals.
130. S. K. Kearsley, S. Sallamack, E. M. Fluder, J. D. Andose, R. T. Mosley, and R. P. Sheridan, J. Chem. Inf. Comput. Sci., 36, 118 (1996). Chemical Similarity Using Physiochemical Property Descriptors.
131. V. J. Gillet, P. Willett, and J. Bradshaw, J. Chem. Inf. Comput. Sci., 38, 165 (1998). Identification of Biological Activity Profiles Using Substructural Analysis and Genetic Algorithms.
132. Spresi database. Daylight Chemical Information Systems Inc., 27401 Los Altos, Suite 370, Mission Viejo, CA 92691. http://www.daylight.com/.
133. D. E. Patterson, R. D. Cramer, A. M. Ferguson, R. D. Clark, and L. E. Weinberger, J. Med. Chem., 39, 3049 (1996). Neighborhood Behavior: A Useful Concept for Validation of Molecular Diversity Descriptors.
134. H. Matter, J. Med. Chem., 40, 1219 (1997). Selecting Optimally Diverse Compounds from Structure Databases: A Validation Study of Two-Dimensional and Three-Dimensional Descriptors.
135. H. Matter, J. Peptide. Res., 52, 305 (1998). A Validation Study of Molecular Descriptors for the Rational Design of Peptide Libraries.
136. 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.
137. R. D. Cramer, S. A DePriest, D. E. Patterson, and P. Hecht, in 3-D QSAR in Drug Design, H. Kubinyi, Ed., ESCOM, Leiden, 1993, pp. 443-485. The Developing Practice of Comparative Molecular Field Analysis.
138. T. I. Oprea and C. L. Waller, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York, 1997, Vol. 11, pp. 127-182. Theoretical and Practical Aspects of Three-Dimensional Quantitative Structure-Activity Relationships.
139. G. Greco, E. Novellino, and Y. C. Martin, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York, 1997, Vol. 11, pp. 183-240. Approaches to Three-Dimensional Quantitative Structure-Activity Relationships.
140. A Ligands of Dual Functionality.
141. J. D. Elliott, M. A. Lago, R. D. Cousins, A. Gao, J. D. Leber, K. F. Erhard, P. Nambi, N. A. Elshourbagy, C. Kumar, J. A. Lee, J. W. Bean, C. W. DeBrosse, D. S. Eggleston, D. P. Brooks, G. Feuerstein, R. R. Ruffolo Jr., J. Weinstock, J. G. Gleason, C. E. Peishoff, and E. H. Ohlstein, J. Med. Chem., 37, 1553 (1994). 1,3-Diarylindan-2-carboxylic Acids, Potent and Selective Non-pepride Endothelin Receptor Antagonists.
142. T. F. Walsh, K. J. Fitch, D. L. Williams Jr., K. L. Murphy, N. A. Nolan, D. J. Pettibone, S. L. Raymond, S. S. O'Malley, B. V. Clineschmidt, D. F. Veber, and W. J. Greenlee, Bioorg. Med. Chem. Lett., 5, 1155 (1995). Potent Dual Antagonists of Endothelin and Angiotensin II Receptors Derived from α-Phenoxyphenylacetic Acids. III.
143. S. A. Mousa and D. A. Cheresh, Drug Discovery Today, 2, 187 (1997). Recent Advances in Cell Adhesion Molecules and Extracellular Matrix Proteins: Potential Clinical Implications.
144. S. D. Pickett, I. M. McLay, and D. E. Clark, J. Chem. Inf. Comput. Sci., 40, 263 (2000). Enhancing the Hit-to-Lead Properties of Lead Optimization Libraries.
145. M. A. Johnson and G. M. Maggiora, Eds., Concepts and Applications of Molecular Similarity. Wiley-Interscience, New York, 1990.
146. J. B. Dunbar, Perspect. Drug Discovery Des., 7/8, 51 (1997). Cluster-Based Selection.
147. M. S. Lajiness, Perspect. Drug Discovery Des., 7/8, 65 (1997). Dissimilarity-Based Compound Selection Techniques.
148. J. H. Wikel and R. E. Higgs, J. Biomol. Screening, 2, 65 (1997). Applications of Molecular Diversity Analysis in High Throughput Screening.
149. R. A. Jarvis and E. A. Patrick, IEEE Trans. Comput., C-22, 1025 (1973). Clustering Using a Similarity Measure Based on Shared Nearest Neighbors.
150. P. R. Menard, R. A. Lewis, and J. S. Mason, J. Chem. Inf. Comput. Sci., 38, 497 (1998). Rational Screening Set Design and Compound Selection: Cascaded Clustering.
151. T. N. Doman, J. M. Cibulskis, M. J. Cibulskis, P. D. McCray, and D. P. Spangler, J. Chem. Inf. Comput. Sci., 36, 1195 (1996). Algorithm5: A Technique for Fuzzy Similarity Clustering of Chemical Inventories.
152. R. Dubes and A. K. Jain, Adv. Comput., 19, 113 (1980). Clustering Methodologies in Exploratory Data Analysis.
153. J. M. Barnard and G. M. Downs, J. Chem. Inf. Comput. Sci., 37, 141 (1997). Chemical Fragment Generation and Clustering Software.
154. F. Murtagh, Multidimensional Clustering Algorithms, Physica-Verlag, Vienna, 1985.
155. L. H. Hall, L. B. Kier, and B. B. Brown, J. Chem. Inf. Comput. Sci., 35, 1074 (1995). Molecular Similarity Based on Novel Atom-Type Electrotopological State Indices.
156. M. J. Ashton, M. C. Jaye, and J. S. Mason, Drug Discovery Today, 1, 71 (1996). New Perspectives in Lead Generation. II. Evaluating Molecular Diversity.
157. D. Bawden, in Chemical Structures 2: The International Language of Chemistry, W. A. Warr, Ed., Springer-Verlag, Berlin, 1993, pp. 383-388. Molecular Dissimilarity in Chemical Information Systems.
158. R. W. Kennard and L. A. Stone, Technometrics, 11, 137 (1969). Computer Aided Design of Experiments.
159. J. D. Holliday, S. S. Ranade, and P. Willett, Quant. Struct.-Act. Relat., 14, 501 (1995). A Fast Algorithm for Selecting Sets of Dissimilar Molecules from Large Chemical Databases.
160. D. B. Turner, S. M. Tyrrell, and P. Willett, J. Chem. Inf. Comput. Sci., 37, 18 (1997). Rapid Quantification of Molecular Diversity for Selective Database Acquisition.
161. J. D. Holliday and P. Willett, J. Biomol. Screening, 1, 145 (1996). Definitions of Dissimilarity for Dissimilarity-Based Compound Selection.
162. M. Snarey, N. K. Terrett, P. Willett, and D. J. Wilton, J. Mol. Graphics, 15, 372 (1997). Comparison of Algorithms for Dissimilarity-Based Compound Selection.
163. D. K. Agrafiotis and V. S. Lobanov, J. Chem. Inf. Comput. Sci., 39, 51 (1999). An Efficient Implementation of Distance-Based Diversity Measures Based on k-d Trees.
164. R. D. Clark, J. Chem. Inf. Comput. Sci., 37, 1181 (1997). OptiSim: An Extended Dissimilarity Selection Method for Finding Diverse Representative Subsets.
165. R. D. Clark and W. J. Langton, J. Chem. Inf. Comput. Sci., 38, 1079 (1998). Balancing Representativeness Against Diversity Using Optimizable K-Dissimilarity and Hierarchical Clustering.
166. M. Hassan, J. P. Bielawski, J. C. Hempel, and M. Waldman, Mol. Diversity, 2, 64 (1996). Optimisation and Visualisation of Molecular Diversity of Combinatorial Libraries.
167. B. D. Hudson, R. M. Hyde, E. Rahr, J. Wood, and J. Osman, Quant. Struct.-Act. Relat., 15, 283 (1996). Parameter Based Methods for Compound Selection from Chemical Data Bases.
168. R. E. Higgs, K. G. Bemis, I. A. Watson, and J. H. Wikei, J. Chem. Inf. Comput. Sci., 37, 861 (1997). Experimental Designs for Selecting Molecules from Large Chemical Databases.
169. S. Anzali, J. Gasteiger, U. Holzgrabe, J. Polanski, J. Sadowski, A. Teckentrup, and M. Wagener, Perspect. Drug Discovery Des., 9/10/11, 273 (1998). The Use of Self-Organizing Neural Networks in Drug Design.
170. H. Bauknecht, A. Zell, H. Bayer, P. Levi, M. Wagener, J. Sadowski, and J. Gasteiger, J. Chem. Inf. Comput. Sci., 36, 1205 (1996). Locating Biologically Active Compounds in Medium-Sized Heterogeneous Datasets by Topological Autocorrelation Vectors: Dopamine and Benzodiazepine Agonists.
171. D. K. Agrafiotis, J. Chem. Inf. Comput. Sci., 37, 841 (1997). Stochastic Algorithms for Maximizing Molecular Diversity.
172. P. Willett, Similarity and Clustering in Chemical Information Systems, Research Studies Press, Letchworth, 1987.
173. J. M. Barnard and G. M. Downs, J. Chem. Inf. Comput. Sci., 32, 644 (1992). Clustering of Chemical Structures on the Basis of Two-Dimensional Similarity Measures.
174. J. W. MacFarlane and D. J. Gans, in Chemometric Methods in Molecular Design, H. van de Waterbeemd, Ed., VCH, Weinheim, 1995, pp. 295-308. Cluster Significance Analysis.
175. D. H. Rouvray, Fuzzy Logic in Chemistry, Academic Press, San Diego, CA, 1997.
176. N. E. Shemetulskis, J. B. Dunbar Jr., B. W. Dunbar, D. W. Moreland, and C. Humbler, J. Comput.-Aided Mol. Des., 9, 407 (1995). Enhancing the Diversity of a Corporate Database Using Chemical Database Clustering and Analysis.
177. CAST-3D Database. Chemical Abstracts Services, Columbus, OH. http://www.cas.org/.
178. Maybridge Database. Daylight Chemical Information Systems Inc., 27401 Los Altos, Suite 370, Mission Viejo, CA 92691. http://www.daylight.com/.
179. Comprehensive Medicinal Chemistry (CMC), Molecular Design Limited, San Leandro, CA 94577. An electronic database version of the Drug Compendium that is Volume 6 of Comprehensive Medicinal Chemistry published by Pergamon Press in March 1990. Contains drugs already on the market.
180. MACCS-II Drug Data Report (MDDR), Molecular Design Limited, San Leandro, CA 94577. An electronic database version of the Prous Science Publishers journal Drug Data Report, extracted from issues starting mid-1988. Contains biologically active compounds in the early stages of drug development.
181. Available Chemicals Directory (ACD), Molecular Design Limited, San Leandro, CA 94577. Contains speciality and bulk chemicals from commercial sources.
182. SPECS/BioSPECS Database; Brandon Associates, Merrimack, NH 03054. Contains chemicals from private sources.
183. R. Nilakantan, N. Bauman, and K. S. Haraki, J. Comput.-Aided Mol. Des., 11, 447 (1997). Diversity Database Assessment: New Ideas, Concepts and Tools.
184. R. Nilakantan, N. Bauman, K. S. Haraki, and R. Venkataraghavan, J. Chem. Inf. Comput. Sci., 30, 65 (1990). A Ring-Based Chemical Structural Query System: Use of a Novel Ring-Complexity Heuristic.
185. F. H. Allen, J. E. Davies, J. J. Galloy, O. Johnson, O. Kennard, C. F. Macrae, E. M. Mitchell, G. F. Mitchell, J. M. Smith and D. G. Watson, J. Chem. Inf. Comput. Sci., 31, 187 (1991). The Development of Version 3 and Version 4 of the Cambridge Structural Database System.
186. G. W. A. Milne, M. C. Nicklaus, J. S. Driscoll, S. Wang, and D. J. Zaharevitz, J. Chem. Inf. Comput. Sci., 34, 1219 (1994). National Cancer Institute Drug Information System 3D Database.
187. A. C. Good and R. A. Lewis, J. Med. Chem., 40, 3926 (1997). New Methodology for Profiling Combinatorial Libraries and Screening Sets: Cleaning Up the Design Process with HARPick.
188. V. J. Gillet, P. Willett, and J. Bradshaw, J. Chem. Inf. Comput. Sci., 37, 731 (1997). The Effectiveness of Reactant Pools for Generating Structurally-Diverse Combinatorial Libraries.
189. J. H. van Drie and M. S. Lajiness, Drug Discovery Today, 3, 274 (1998). Approaches to Virtual Library Design.
190. J. H. Kalivas, Chemom. Intell. Lab. Syst., 15, 1 (1992). Optimization Using Variations of Simulated Annealing.
191. R. Judson, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., VCH Publishers, New York, 1997, Vol. 10, pp. 1-73. Genetic Algorithms and Their Use in Chemistry.
192. R. D. Brown and D. E. Clark, Expert Opin. Ther. Patents, 8, 1447 (1998). Genetic Diversity: Applications of Evolutionary Algorithms to Combinatorial Library Design.
193. L. Weber, Curr. Opin. Chem. Biol., 2, 381 (1998). Applications of Genetic Algorithms in Molecular Diversity.
194. L. Weber, Drug Discovery Today, 3, 379 (1998). Evolutionary Combinatorial Chemistry: Application of Genetic Algorithms.
195. R. A. Lewis, A. C. Good, and S. D. Pickett, in Computer-Assisted Lead Finding and Optimization: Current Tools for Medicinal Chemistry, H. van de Waterbeemd, B. Testa, and G. Folkers, Eds., Wiley-VCH, Weinheim, 1997, pp. 135-156. Quantification of Molecular Similarity and Its Application to Combinatorial Chemistry.
196. V. J. Gillet, P. Willett, J. Bradshaw, and D. V. S. Green, J. Chem. Inf. Comput. Sci., 39, 169 (1999). Selecting Combinatorial Libraries to Optimize Diversity and Physical Properties.
197. R. P. Sheridan and S. K. Kearsley, J. Chem. Inf. Comput. Sci., 35, 310 (1995). Using a Genetic Algorithm to Suggest Combinatorial Libraries.
198. S. J. Cho, W. Zheng, and A. Tropsha, J. Chem. Inf. Comput. Sci., 38, 259 (1998). Rational Combinatorial Library Design. 2. Rational Design of Targeted Combinatorial Peptide Libraries Using Chemical Similarity Probe and the Inverse QSAR Approaches.
199. S. D. Pickett, unpublished work, 1999.
200. P. J. Brown, T. A. Smith-Oliver, P. S. Charifson, N. C. O. Tomkinson, A. M. Fivush, D. D. Sternbach, L. E. Wade, L. Orband-Miller, D. J. Parks, S. G. Blanchard, S. A. Kliewer, J. H. Lehmann, and T. M. Willson, Chem. Biol., 4, 909 (1997). Identification of Peroxisome Proliferator-Activated Receptor Ligands from a Biased Chemical Library.
201. F. R. Salemme, J. Spurlino, and R. Bone, Structure, 5, 319 (1997). Serendipity Meets Precision: The Integration of Structure-Based Drug Design and Combinatorial Chemistry for Efficient Drug Discovery.
202. J. Li, C. W. Murray, B. Waszkowycz, and S. C. Young, Drug Discovery Today, 3, 105 (1998). Targeted Molecular Diversity in Drug Discovery: Integration of Structure-Based Design and Combinatorial Chemistry.
203. M. A. Murcko, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York, 1997, Vol. 11, pp. 1-66. Recent Advances in Ligand Design Methods.
204. D. E. Clark, C. W. Murray, and J. Li, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., Wiley-VCH, New York, 1997, Vol. 11, pp. 67-125. Current Issues in De Novo Molecular Design.
205. A. Rockwell, M. Melden, R. A. Copeland, K. Hardman, C. P. Decicco, and W. F. DeGrado, J. Am. Chem. Soc., 118, 10337 (1996). Complementarity of Combinatorial Chemistry and Structure-Based Ligand Design: Application to the Discovery of Novel Inhibitors of Matrix Metalloproteinases.
206. A. P. Combs, T. M. Kapoor, S. B. Feng, J. K. Chen, L. F. Daudesnow, and S. L. Schreiber, J. Am. Chem. Soc., 118, 287 (1996). Protein Structure-Based Combinatorial Chemistry: Discovery of Non-peptide Binding Elements to Src SH3 Domain.
207. T. C. Norman, N. S. Gray, J. T. Koh, and P. G. Schultz, J. Am. Chem. Soc., 118, 7430 (1996). A Structure-Based Library Approach to Kinase Inhibitors.
208. T. M. Kapoor, A. H. Andreotti, and S. L. Schreiber, J. Am. Chem. Soc., 120, 23 (1998). Exploring the Specificity Pockets of Two Homologous SH3 Domains Using Structure-Based, Split-Pool Synthesis and Affinity-Based Selection.
209. J. P. Morken, T. M. Kapoor, S. Feng, F. Shirai, and S. L. Schreiber, J. Am. Chem. Soc., 120, 30 (1998). Exploring the Leucine-Proline Binding Pocket of the Src SH3 Domain Using Structure-Based, Split-Pool Synthesis and Affinity-Based Selection.
210. S. F. Brady, K. J. Stauffer, W. C. Lumma, G. M. Smith, H. G. Ramjit, S. D. Lewis, B. J. Lucas, S. J. Gardell, E. A. Lyle, S. D. Appleby, J. J. Cook, M. A. Holahan, M. T. Stranieri, J. J. Lynch Jr., J. H. Lin, I.-W. Chen, K. Vastag, A. M. Naylor-Olsen, and J. P. Vacca, J. Med. Chem., 41, 401 (1998). Discovery and Development of the Novel Potent Orally Active Thrombin Inhibitor N-(9-Hydroxy-9-fluorenecarboxy)prolyl trans-4-Aminocyclohexylmethyl Amide (L-372,460): Coapplication of Structure-Based Design and Rapid Multiple Analog Synthesis on Solid Support.
211. C. Illig, S. Eisennagel, R. Bone, A. Radzicka, L. Murphy, T. Randle, J. Spurlino, F. R. Salemme, and R. M. Soll, Med. Chem. Res., 4/5, 244 (1998). Expanding the Envelope of Structure-Based Drug Design Using Chemical Libraries: Application to Small Molecule Inhibitors of Thrombin.
212. D. S. Dhanoa, R. M. Soll, Z. Wu, N. Subasinghe, J. Rinker, J. Hoffman, S. Eisennagel, T. Graybill, R. Bone, A. Radzicka, L. Murphy, and F. R. Salemme, Med. Chem. Res., 4/5, 187 (1998). Serine Proteases - Directed Small Molecule Probe Libraries.
213. S.-H. Kim, Pure Appl. Chem., 70, 555 (1998). Structure-Based Inhibitor Design for CDK2, a Cell Cycle Controlling Protein.
214. M. Whittaker, Curr. Opin. Chem. Biol., 2, 386 (1998). Discovery of Protease Inhibitors Using Targeted Libraries.
215. A. K. Szardenings, D. Harris, S. Lam, L. Shi, D. Tien, Y. Wang, D. V. Patel, M. Navre, and D. A. Campbell, J. Med. Chem., 41, 2194 (1998). Rational Design and Combinatorial Evaluation of Enzyme Inhibitor Scaffolds: Identification of Novel Inhibitors of Matrix Metalloproteinases.
216. K. D. Stewart, S. Loren, L. Frey, E. Otis, V. Klinghofer, and K. I. Hulkower, Bioorg. Med. Chem. Lett., 8, 529 (1998). Discovery of a New Cyclooxygenase-2 Lead Compound Through 3-D Database Searching and Combinatorial Chemistry.
217. T. L. Graybill, D. K. Agrafiotis, R. Bone, C. R. Illig, E. P. Jaeger, K. T. Locke, T. Lu, J. M. Salvino, R. M. Soll. J. C. Spurlino, N. Subasinghe, B. E. Tomczuk, and F. R. Salemme, in Molecular Diversity and Combinatorial Chemistry: Libraries and Drug Discovery, I. M. Chaiken and K. D. Janda, Eds., American Chemical Society, Washington, DC, 1996, pp. 16-27. Enhancing the Drug Discovery Process by Integration of High-Throughput Chemistry and Structure-Based Drug Design.
218. E. J. Martin and R. E. Critchlow, J. Comb. Chem., 1, 32 (1999). Beyond Mere Diversity: Tailoring Combinatorial Libraries for Drug Discovery.
219. G. M. Rishton, Drug Discovery Today, 2, 382 (1997). Reactive Compounds and In Vitro False Positives in HTS.
220. A. D. Rodrigues, Pharm. Res., 14, 1504 (1997). Preclinical Drug Metabolism in the Age of High-Throughput Screening: An Industrial Perspective.
221. J. H. Lin and A. Y. H. Lu, Pharmacol. Rev., 49, 403 (1997). Pharmacokinetics and Metabolism in Drug Discovery and Development.
222. M. H. Tarbit and J. Berman, Curr. Opin. Chem. Biol., 2, 411 (1998). High-Throughput Approaches for Evaluating Absorption, Distribution, Metabolism and Excretion Properties of Lead Compounds.
223. P. J. Sinko, Curr. Opin. Drug Discovery Dev., 2, 42 (1999). Drug Selection in Early Drug Development: Screening for Acceptable Pharmacokinetic Properties Using Combined In Vitro and Computational Approaches.
224. C. A. Lipinski, F. Lombardo, B. W. Dominy, and P. J. Feeney, Adv. Drug. Delivery Rev., 23, 3 (1997). Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings.
225. I. Moriguchi, S. Hirono, Q. Liu, I. Nakagome, and Y. Matsushita, Chem. Pharm. Bull., 40, 127 (1992). Simple Method of Calculating Octanol/Water Partition Coefficient.
226. K. Palm, K. Luthmann, A.-L. Ungell, G. Strandlund, and P. Artursson, J. Pharm. Sci., 85, 32 (1996). Correlation of Drug Absorption with Molecular Surface Properties.
227. K. Palm, P. Stenberg, K. Luthmann, and P. Artursson, Pharm. Res., 14, 568 (1997). Polar Molecular Surface Properties Predict the Intestinal Absorption of Drugs in Humans.
228. K. Palm, K. Luthman, A.-L. Ungell, G. Strandlund, F. Beigi, P. Lundahl, and P. Artursson, J. Med. Chem., 41, 5382 (1998). Evaluation of Dynamic Polar Molecular Surface Area as Predictor of Drug Absorption: Comparison with Other Computational and Experimental Predictors.
229. S. Winiwarter, N. M. Bonham, F. Ax, A. Hallberg, H. Lennernäs, and A. Karlen, J. Med. Chem., 41, 4939 (1998). Correlation of Human Jejunal Permeability (In Vivo) of Drugs with Experimentally and Theoretically Derived Parameters. A Multivariate Data Analysis Approach.
230. D. E. Clark, J. Pharm. Sci., 88, 807 (1999). Rapid Calculation of Polar Molecular Surface Area and Its Application to the Prediction of Transport Phenomena. 1. Prediction of Intestinal Absorption.
231. D. E. Clark, J. Pharm. Sci., 88, 815 (1999). Rapid Calculation of Polar Molecular Surface Area and Its Application to the Prediction of Transport Phenomena. 2. Prediction of Blood-Brain Barrier Penetration.
232. Y. C. Martin, Perspect. Drug Discovery Des., 7/8, 159 (1997). Challenges and Prospects for Computational Aids to Molecular Diversity.
233. J. S. Mason and M. A. Hermsmeier, Curr. Opin. Chem. Biol., 3, 342 (1999). Diversity Assessment.
234. C. A. Parks, G. M. Crippen, and J. G. Topliss, J. Comput.-Aided Mol. Des., 12, 441 (1998). The Measurement of Molecular Diversity by Receptor Site Interaction Simulation.
235. D. A. Thorner, D. J. Wild, P. Willett, and P. M. Wright, Perspect. Drug Discovery Des., 9/10/11, 301 (1998). Calculation of Structural Similarity by the Alignment of Molecular Electrostatic Potentials.
236. Ajay, W. P. Walters, and M. A. Murcko, J. Med. Chem., 41, 3314 (1998). Can We Learn to Distinguish Between Drug-like and Non-drug-like Molecules?
237. J. Sadowski and H. Kubinyi, J. Med. Chem., 41, 3325 (1998). A Scoring Scheme for Discriminating Between Drugs and Nondrugs.
238. A. K. Ghose, V. N. Viswanadhan, and J. J. Wendoloski, J. Comb. Chem., 1, 55 (1999). A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases.
239. J. Moult, T. Hubbard, S. H. Bryant, K. Fidelis, and J. T. Pedersen, Proteins: Struct., Funct., Genet., Suppl. 1, 2 (1997). Critical Assessment of Methods of Protein Structure Prediction (CASP): Round II.
240. H.-J. Böhm, J. Comput.-Aided Mol. Des., 12, 309 (1998). Prediction of Binding Constants of Protein Ligands: A Fast Method for the Prioritization of Hits Obtained from De Novo Design or 3-D Database Search Programs.
241. I. Muegge and Y. C. Martin, J. Med. Chem., 42, 791 (1999). A General and Fast Scoring Function for Protein-Ligand Interactions: A Simplified Potential Approach.
242. R. H. Smith Jr., W. L. Jorgensen, J. Tirado-Rives, M. L. Lamb, P. A. J. Janssen, C. J. Michejda, and M. B. K. Smith, J. Med. Chem., 41, 5272 (1998). Prediction of Binding Affinities for TIBO Inhibitors of HIV-1 Reverse Transcriptase Using Monte Carlo Simulations in a Linear Response Method.
243. T. Hansson, J. Marelius, and J. Åqvist, J. Comput.-Aided Mol. Des., 12, 27 (1998). Ligand Binding Affinity Prediction by Linear Interaction Energy Methods.
244. T. P. Straatsma, in Reviews in Computational Chemistry, K. B. Lipkowitz and D. B. Boyd, Eds., VCH Publishers, New York, 1996, Vol. 9, pp. 81-127. Free Energy by Molecular Simulation.
245. J. M. Barnard and G. M. Downs, Perspect. Drug Discovery Des., 7/8, 13 (1997). Computer Representation and Manipulation of Combinatorial Libraries.
246. X. Chen, A. Rusinko, and S. S. Young, J. Chem. Inf. Comput. Sci., 38, 1054 (1998). Recursive Partitioning Analysis of a Large Structure-Activity Data Set Using Three-Dimensional Descriptors.
247. H. Gao, C. Williams, P. Labute, and J. Bajorath, J. Chem. Inf. Comput. Sci., 39, 164 (1999). Binary Quantitative Structure-Activity Relationship (QSAR) Analysis of Estrogen Receptor Ligands.
248. R. S. Pearlman (University of Texas at Austin), private communication, 1999.
249. DiverseSolutions. Distributed by Tripos, Inc., 1699 South Hanley Road, St. Louis, MO 63144, on behalf of the Laboratory for Molecular Graphics and Theoretical Modeling, College of Pharmacy, University of Texas at Austin, Austin, TX, 78712.