Modeling and Informatics in Drug Design

Preclinical Development Handbook: ADME and Biopharmaceutical Properties

Volumn , Issue , 2008, Pages 1-45

  Bharatam, Prasad V ^a   Khanna, Smriti ^a   Francis, Sandrea M ^a  

^a NATIONAL INSTITUTE OF PHARMACEUTICAL EDUCATION AND RESEARCH NIPER   (India)

Author keywords

Computer aided drug design (CADD); Multiconfiguration self consistent field (MCSCF); Threading or remote homology modeling

Indexed keywords

EID: 84923573840     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470249031.ch1     Document Type: Chapter

References (129)

1. http://www.cambridgesoft.com.
2. http://www.acdlabs.com/download/chemsk.html.
3. http://www.chemsw.com.
4. Goodman JM. Chemical Applications of Molecular Modeling. Cambridge, UK:Royal Society of Chemistry; 1998.
5. Holtje HD, Sippl W, Rognan D. Molecular Modeling Basic Principles and Applications, 2nd ed. Weinheim:Wiley-VCH Verlag GmbH; 2003.
6. Leach AR, Gillet VJ. An Introduction to Chemoinformatics. Dordrecht:Kluwer Academics ; 2003.
7. Levine IN. Quantum Chemistry, 4th ed. Englewood Cliffs, NJ:Prentice-Hall; 1991.
8. Szabo A, Ostlund S. Modern Quantum Chemistry. New York:MacMillan; 1982.
9. Foresman JB, Frisch AE. Exploring Chemistry with Electronic Structure Methods, 2nd ed. Pittsburgh:Gaussian Inc.; 1995.
10. Gasteiger J, Engel T. Chemoinformatics:A Textbook. Weinheim:Wiley-VCH Verlag GmbH; 2003.
11. Howard AE, Kollman PA. An analysis of current methodologies for conformational searching of complex molecules. J Med Chem 1998;31:1669-1675.
12. Lipton M, Still WC. The multiple minimum problem in molecular modeling. Tree searching internal coordinate conformational space. J Comput Chem 1988;9:343-355.
13. Dammkoehler RA, Karasek SF, Shands EF, Marshall GR. Constrained search of conformational hyperspace. J Comput Aided Mol Des 1989;3:3-21.
14. Saunders M. Stochastic exploration of molecular mechanics energy surfaces. Hunting for the global minimum. J Am Chem Soc 1987;109:3150-3152.
15. Saunders M. Stochastic search for the conformations of bicyclic hydrocarbons. J Comput Chem 1989;10:203-208.
16. Ferguson DM, Raber DJ. A new approach to probing conformational space with molecular mechanics:random incremental pulse search. J Am Chem Soc 1989;111:4371-4378.
17. Chang G, Guida WC, Still WC. An internal-coordinate Monte Carlo method for searching conformational space. J Am Chem Soc 1989;111:4379-4386.
18. Saunders M, Houk KN, Wu YD, Still WC, Lipton M, Chang G, Guida, WC. Conformations of cycloheptadecane. A comparison of methods for conformational searching. J Am Chem Soc 1990;112:1419-1427.
19. Lybrand TP, Computer simulation of biomolecular systems using molecular dynamics and free energy perturbation methods, in Reviews in Computational Chemistry. Hoboken, NJ:Wiley-VCH; 1990, pp 295-320.
20. Bourne PE, Weissig H. Structural Bioinformatics (Methods of Biochemical Analysis). Hoboken, NJ:Wiley-Liss; 2003.
21. Clote P, Backofen R. Computational Molecular Biology:An Introduction. Hoboken, NJ:Wiley; 2000.
22. Dunbrack RL Jr, Karplus M. Conformational analysis of the backbone-dependent rotamer preferences of protein sidechains. Nat Struct Biol 1994;1:334-340.
23. Park BH, Levitt M. The complexity and accuracy of discrete state models of protein structure. J Mol Biol 1995;249:493-507.
24. Sternberg MJE. Protein Structure Prediction:A Practical Approach (The Practical Approach Series). New York:Oxford University Press; 1997.
25. Randíc M. Characterization of molecular branching. J Am Chem Soc 1975;97:6609-6615.
26. Kier LB, Hall LH. Molecular Connectivity in Chemistry and Drug Research. New York:Academic Press; 1976.
27. Kier LB, Hall LH. Molecular Connectivity in Structure-Activity Analysis. Hoboken, NJ:Wiley; 1986.
28. Müller WR, Szymanski K, Knop JV, Trinajstic N. An algorithm for construction of the molecular distance matrix. J Comput Chem 1987;8:170-173.
29. Wiener H. Structural determination of paraffin boiling points. J Am Chem Soc 1947;69:17-20.
30. Hosoya H. Topological index. A newly proposed quantity characterizing the topological nature of structural isomers of saturated hydrocarbons. Bull Chem Soc Jpn 1971;44:2332-2339.
31. Bonchev D. Information Theoretic Indices for Characterization of Chemical Structures. Hoboken, NJ:Wiley; 1983.
32. Balaban AT. Highly discriminating distance-based topological index. Chem Phys Lett 1982;89:399-404.
33. Kier LB. A shape index from molecular graphs. Quant Struct-Activity Relat 1985;4:109-116.
34. Kier LB, Hall LH, Frazer JW. An index of electrotopological state for atoms in molecules. J Math Chem 1991;7:229-241.
35. Todeschini R, Consonni V. Handbook of Molecular Descriptors. Weinheim:Wiley-VCH Verlag GmbH; 2000.
36. Karelson M, Lobanov VS, Katritzky AR. Quantum-chemical descriptors in QSAR/ QSPR studies. Chem Rev 1996;96:1027-1044.
37. Abraham DJ. Burger's Medicinal Chemistry & Drug Discovery, 6th ed. Hoboken, NJ:Wiley-Interscience; 2003.
38. Hansch C, Leo A, Heller SR. Exploring QSAR:Volume 1:Fundamentals and Applications in Chemistry and Biology. Washington DC:American Chemical Society; 1995.
39. Kubinyi H, QSAR:Hansch analysis and related approaches. In Methods and Principles in Medicinal Chemistry. Weinheim:VCH; 1993.
40. Kubinyi H, QSAR In Drug Design. Theory, Methods and Applications. Leiden:ESCOM; 1993.
41. Cramer RDI, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 1988;110:5959-5967.
42. Klebe G, Abraham U, Meitzner T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 1994;37:4130-4146.
43. Robinson DD, Winn PJ, Lyne PD, Richards WG. Self-organizing molecular field analysis:a tool for structure-activity studies. J Med Chem 1999;42:573-583.
44. Silverman BD, Platt DE. Comparative molecular moment analysis(CoMMA):3D-QSAR without molecular superposition. J Med Chem 1996;39:2129-2140.
45. Kubinyi H. Comparative molecular field analysis (CoMFA), in The Encyclopedia of Computational Chemistry. Chichester:Wiley; 1998, pp 448-460.
46. Wise M, Cramer RD, Smith D, Exman I. Progress in three-dimensional drug design:the use of real-time colour graphics and computer postulation of bioactive molecules in DYLOMMS. In Quantitative Approaches to Drug Design. Amsterdam:Elsevier; 1983, pp 145-146.
47. Pastor M, Cruciani G, Watson KAA. Strategy for the incorporation of water molecules present in a ligand binding site into a three-dimensional quantitative structure-activity relationship analysis. J Med Chem 1997;40:4089-4102.
48. Polanski J, Walczak B. The comparative molecular surface analysis(COMSA):a novel tool for molecular design. Comput Chem 2000;24:615-625.
49. Ivanciuc O, Ivanciuc T, Cabrol-Bass D. Comparative receptor surface analysis (CoRSA) model for calcium channel antagonists. SAR QSAR Environ Res 2002;12:93-111.
50. Hariprasad V, Kulkarni VM. A proposed common spatial pharmacophore and the corresponding active conformations of some peptide leukotriene receptor antagonists. J Comput Aided Mol Des 1996;10:284-292.
51. Vedani A, Briem H, Dobler M, Dollinger H, McMasters DR. Multiple-conformation and protonation-state representation in 4D-QSAR:the neurokinin-1 receptor system. J Med Chem 2000;43:4416-4427.
52. Vedani A, Dobler M, Dollinger H, Hasselbach KM, Birke F, Lill MA. Novel ligands for the chemokine receptor-3(CCR3):a receptor-modeling study based on 5D-QSAR. J Med Chem 2005;48:1515-1527.
53. Vedani A, Dobler M. 5D-QSAR:the key for simulating induced fit? J Med Chem 2002;45:2139-2149.
54. Vedani A, Dobler M, Lill MA. Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor. J Med Chem 2005;48:3700-3703.
55. Martin YC, Bures M, Dahaner, E. A fast approach to pharmacophore mapping and its applications to dopaminergic and benzodiazepine agonists. J Comput Aided Mol Des 1993;7:83-102.
56. Marriott DP, Dougall IB, Meghani P, Liu Y-J, Flower DR. Lead generation using pharmacophore mapping and three-dimensional database searching:application to muscarinic M(3) receptor antagonists. J Med Chem 1999;42:3210-3216.
57. Sutter J, Güner OF, Hoffmann R, Li H, Waldman M. P harmacophore, Perception Development and Use In Drug Design. La Jolla, CA:International University Line, 2000, pp 504-506.
58. SYBYL7.0, Tripos Inc, 1699 South Hanley Rd, St Louis, MO 631444, USA. http://www. tripos.com.
59. Gardiner EJ, Artymiuk PJ, Willett P. Clique-detection algorithms for matching three-dimensional molecular structures. J Mol Graph Model 1997;15:245-253.
60. Sprague PW. Automated chemical hypothesis generation and database searching with CATALYST. In Perspectives in Drug Discovery and Design. Leiden:ESCOM Science; 1995, pp 1-20.
61. CATALYST4.10, Biosyn-MSI, San Diego, CA, USA, 2005. http://www.accelrys.com.
62. Bajorath J. Virtual screening in drug discovery:methods, expectations and reality. Curr Drug Discov 2002, pp 24-28.
63. Klebe G. Virtual Screening:An Alternative or Complement to High Throughput Screening? Berlin:Springer; 2000.
64. Leach AR. Molecular Modelling:Principles and Applications, 2nd ed. Boston:Addison Wesley Longman, Harlow; 1996.
65. Cohen N. Guidebook on Molecular Modeling in Drug Design. San Drego, CA:Academic Press; 1996.
66. Charifson PS. Practical Application of Computer-Aided Drug Design. New York:Marcel Dekker; 1997.
67. Schneider G, Bohm H-J. Virtual screening and fast automated docking methods. Drug Discov Today 2002;7:64-70.
68. Rarey M, Kramer B, Lengauer T, Klebe G. A fast flexible docking methods using an incremental construction algorithm. J Mol Biol 1996;261:470-489.
69. Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE. A geometric approach to macromolecule-ligand interactions. J Mol Biol 1982;161:269-288.
70. Morris GM, Goodsell DS, Huey R, Olson AJ. Distributed automated docking of flexible ligands to proteins:parallel applications of AutoDock2.4. J Comput Aided Mol Des 1996;10:293-304.
71. Moon JB, Howe WJ. Computer design of bioactive molecules:a method for receptor-based de novo ligand design. Proteins 1991;11:314-328.
72. Goodford PJ. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 1985;28:849-857.
73. Lauri G, Bartlett PA. CAVEAT:a program to facilitate the design of organic molecules. J Comput Aided Mol Des 1994;8:51-66.
74. Böhm HJ. LUDI:rule-based automatic design of new substituents for enzyme inhibitor leads. J Comput Aided Mol Des 1992;6:593-606.
75. Böhm HJ. The computer program LUDI:a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 1992;6:61-78.
76. Böhm HJ. A novel computational tool for automated structure-based drug design. J Mol Recognit 1993;6:131-137.
77. Böhm HJ. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J Comput Aided Mol Des 1994;8:243-256.
78. Rotstein SH, Murcko MA. GroupBuild:a fragment-based method for de novo drug design. J Med Chem 1993;36:1700-1710.
79. Gillet V, Johnson AP, Mata P, Sike S, Williams P. SPROUT:a program for structure generation. J Comput Aided Mol Des 1993;7:127-153.
80. Gund P, Sigal N. Applying information systems to high-throughput screening and analysis. In Pharmainformatics:A Trend Guide. Drug Discor Today 1999;(Suppl), 25-29.
81. Bharatam PV. Pharmacoinformatics:IT solutions for drug discovery and development, CRIPS (Current Research and Information on Pharmaceutical Sciences), 2003;4:2-5.
82. Hanson CW. Healthcare Informatics. New York:McGraw-Hill Professional; 2005.
83. Goodman KW. Ethics, Computing, and Medicine:Informatics and the Transformation of Health Care, 1st ed. Cambridge, UK:Cambridge University Press; 1998.
84. Saba VK, McCormick KA. Essentials of Nursing Informatics, 4th ed. New York:McGraw-Hill Medical; 2005.
85. Gadre SR, Shirsat RN. Electrostatics of Atoms and Molecules. Hyderabad:Universities Press (India) Limited; 2000.
86. Oprea TI. Chemoinformatics in drug discovery. In Methods and Principles in Medicinal Chemistry, Vol 23. Weinheim:Wiley-VCH Verlag GmbH; 2005.
87. Gasteiger J. Handbook on chemoinformatics:from data to knowledge, In Representation of Molecular Structures. Hoboken, NJ:Wiley; 2003.
88. Bajorath J. Chemoinformatics:concepts, methods, and tools for drug discovery. In Methods in Molecular Biology. Totowa, NJ:Humana Press; 2004.
89. Mount DW. Bioinformatics:Sequence and Genome Analysis. Cold Spring Harbor, NY:Cold Spring Harbor Laboratory Press; 2001.
90. Xenarios I, Salwinski L, Duan XJ, Higney P, Kim SM, Eisenberg D. DIP, the Database of Interacting Proteins:a research tool for studying cellular networks of protein interactions. Nucleic Acids Res 2002;30:303-305.
91. Breitkreutz BJ, Stark C, Tyers M. The GRID:the General Repository for Interaction Datasets. Genome Biol 2003;4:R23.
92. Bader GD, Betel D, Hogue CW. BIND:the Biomolecular Interaction Network Database. Nucleic Acids Res 2003;31:248-250.
93. Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, Muthusamy B, Gandhi TK, Chandrika KN, Deshpande N, Suresh S, Rashmi BP, Shanker K, Padma N, Niranjan V, Harsha HC, Talreja N, Vrushabendra BM, Ramya MA, Yatish AJ, Joy M, Shivashankar HN, Kavitha MP, Menezes M, Choudhury DR, Ghosh N, Saravana R, Chandran S, Mohan S, Jonnalagadda CK, Prasad CK, Kumar-Sinha C, Deshpande KS, Pandey A. Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res 2004;32:D497-501.
94. Marcotte EM, Xenarios I, Eisenberg D. Mining literature for protein-protein interactions. Bioinformatics 2001;17:359-363.
95. Wilcox A, Hripcsak G. Classification algorithms applied to narrative reports. Proc AMIA Symp 1999;455-459.
96. STRING, http://string.embl.de/.
97. von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B. STRING:a database of predicted functional associations between proteins. Nucleic Acids Res 2003;31:258-261.
98. Tien AC, Lin MH, Su LJ, Hong YR, Cheng TS, Lee YC, Lin WJ, Still IH, Huang CY. Identification of the substrates and interaction proteins of aurora kinases from a protein-protein interaction model. Mol Cell Proteomics 2004;3:93-104.
99. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 1997;23:3-25.
100. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, Woods R, Paus T, Simpson G, Pike B, Holmes C, Collins L, Thompson P, MacDonald D, Iacoboni M, Schormann T, Amunts K, Palomero-Gallagher N, Geyer S, Parsons L, Narr K, Kabani N, Le Goualher G, Boomsma D, Cannon T, Kawashima R, Mazoyer B. A probabilistic atlas and reference system for the human brain:International Consortium for Brain Mapping (ICBM). Philos Trans R Soc Lond B Biol Sci 2001;356:1293-1322.
101. Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K, Woods R, Paus T, Simpson G, Pike B, Holmes C, Collins L, Thompson P, MacDonald D, Iacoboni M, Schormann T, Amunts K, Palomero-Gallagher N, Geyer S, Parsons L, Narr K, Kabani N, Le Goualher G, Feidler J, Smith K, Boomsma D, Hulshoff Pol H, Cannon T, Kawashima R, Mazoyer B. A four-dimensional probabilistic atlas of the human brain. J Am Med Inform Assoc 2001;8:401-430.
102. Dickson J, Drury H, Van Essen DC. The surface management system (SuMS) database:a surface-based database to aid cortical surface reconstruction, visualization and analysis. Philos Trans R Soc Lond B Biol Sci 2001;356:1277-1292.
103. Van Essen DC. Windows on the brain:the emerging role of atlases and databases in neuroscience. Curr Opin Neurobiol 2002;12:574-579.
104. Burns GA. Knowledge management of the neuroscientific literature:the data model and underlying strategy of the NeuroScholar system. Philos Trans R Soc Lond B Biol Sci 2001;356:1187-1208.
105. Shepherd GM, Mirsky JS, Healy MD, Singer MS, Skoufos E, Hines MS, Nadkarni P M, Miller PL. The Human Brain Project:neuroinformatics tools for integrating, searching and modeling multidisciplinary neuroscience data. Trends Neurosci 1998;21:460-468.
106. Rammensee HG. Immunoinformatics:bioinformatic strategies for better understanding of immune function. Introduction. Novartis Found Symp 2003;254:1-2.
107. Brusic V, Zeleznikow J, Petrovsky N. Molecular immunology databases and data repositories. J Immunol Methods 2000;238:17-28.
108. Robinson J, Waller MJ, Parham P, de Groot N, Bontrop R, Kennedy LJ, Stoehr P, Marsh SG. IMGT/HLA and IMGT/MHC:sequence databases for the study of the major histocompatibility complex. Nucleic Acids Res 2003;31:311-314.
109. Lefranc MP. IMGT, the international ImMunoGeneTics database. Nucleic Acids Res 2003;31:307-310.
110. Giudicelli V, Lefranc MP. Ontology for immunogenetics:the IMGT-ONTOLOGY. Bioinformatics 1999;15:1047-1054.
111. Petrovsky N, Schonbach C, Brusic V. Bioinformatic strategies for better understanding of immune function. In Silico Biol 2003;3:411-416.
112. Lefranc MP. IMGT, The international ImMunoGeneTics Information System. Methods Mol Biol 2004;248:27-49.
113. Lefranc MP, Giudicelli V, Ginestoux C, Bosc N, Folch G, Guiraudou D, Jabado-Michaloud J, Magris S, Scaviner D, Thouvenin V, Combres K, Girod D, Jeanjean S, Protat C, Yousfi-Monod M, Duprat E, Kaas Q, Pommie C, Chaume D, Lefranc G. IMGT-ONTOLOGY for immunogenetics and immunoinformatics. In Silico Biol 2004;4:17-29.
114. Lefranc MP. IMGT-ONTOLOGY and IMGT databases, tools and Web resources for immunogenetics and immunoinformatics. Mol Immunol 2004;40:647-660.
115. Brusic V, Zeleznikow J. Artificial neural network applications in immunology, in Proceedings of the International Joint Conference on Neural Networks, 1999;5:3685-3689.
116. De Groot AS, Bosma A, Chinai N, Frost J, Jesdale BM, Gonzalez MA, Martin W, Saint-Aubin C. From genome to vaccine:in silico predictions, ex vivo verification. Vaccine 2001;19:4385-4395.
117. De Groot AS, Sbai H, Aubin CS, McMurry J, Martin W. Immuno-informatics:mining genomes for vaccine components. Immunol Cell Biol 2002;80:255-269.
118. de Castro LN, Timmis JI. Artificial immune systems:a novel paradigm to pattern recognition. In Artificial Neural Networks in Pattern Recognition. Paisley, UK:University of Paisley; 2002, pp 67-84.
119. Korolev D, Balakin KV, Nikolsky Y, Kirillov E, Ivanenkov YA, Savchuk NP, Ivashchenko AA, Nikolskaya T. Modeling of human cytochrome P450-mediated drug metabolism using unsupervised machine learning approach. J Med Chem 2003;46:3631-3643.
120. Erhardt PW. A human drug metabolism database:potential roles in the quantitative predictions of drug metabolism and metabolism-related drug-drug interactions. Curr Drug Metab 2003;4:411-422.
121. Langowski J, Long A. Computer systems for the prediction of xenobiotic metabolism. Adv Drug Deliv Rev 2002;54:407-415.
122. Barratt MD, Rodford RA. The computational prediction of toxicity. Curr Opin Chem Biol 2001;5:383-388.
123. Pearl GM, Livingston-Carr S, Durham SK. Integration of computational analysis as a sentinel tool in toxicological assessments. Curr Top Med Chem 2001;1:247-255.
124. Dearden JC, Barratt MD, Benigni R, Bristol DW, Combes RD, Cronin MTD, Judson PN, Payne MP, Richard AM, Tichy M, Worth AP, Yourick JJ. The development and validation of expert systems for predicting toxicity. ATLA 1997;25:223-252.
125. Schultz TW, Cronin MTD, Walker JD, Aptula AO. Quantitative structure-activity relationships (QSARs) in toxicology:a historical perspective. J Mol Structure (Theochem) 2003;622:1-22.
126. Covitz PA, Hartel F, Schaefer C, De Coronado S, Fragoso G, Sahni H, Gustafson S, Buetow KH. caCORE:a common infrastructure for cancer informatics. Bioinformatics 2003;19:2404-2412.
127. Silva JS, Ball MJ, Douglas JV. The Cancer Informatics Infrastructure(CII):an architecture for translating clinical research into patient care. Medinfo 2001;10:114-117.
128. Hubbard SM, Setser A. The Cancer Informatics Infrastructure:a new initiative of the National Cancer Institute. Semin Oncol Nurs 2001;17:55-61.
129. Kihara D, Yang YD, Hawkin T. Bioinformatics resources for cancer research with an emphasis on gene function and structure prediction tools. Cancer Informatics 2006; pp 25-35.