Computer-Aided Drug Discovery and Development

Methods in Molecular Biology

Volumn 716, Issue , 2011, Pages 23-38

  Zhang, Shuxing ^a  

^a UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

Computer aided drug discovery; High throughput screening; Ligand based drug design; Molecular docking; Quantitative structure activity relationship; Structure based drug design, Virtual screening

Indexed keywords

LIGAND;

COMPUTER AIDED DESIGN; DRUG DESIGN; INSTRUMENTATION; REVIEW;

COMPUTER-AIDED DESIGN; DRUG DESIGN; LIGANDS;

EID: 79956188485     PISSN: 10643745     EISSN: 19406029     Source Type: Book Series    
DOI: 10.1007/978-1-61779-012-6_2     Document Type: Chapter

References (83)

1. Workman, P. (2003). How much gets there and what does it do?: The need for better pharmacokinetic and pharmacodynamic end-points in contemporary drug discovery and development. Curr Pharm Des. 9: 891–902.
2. Brown, D. & Superti-Furga, G. (2003). Rediscovering the sweet spot in drug discovery. Drug Discov Today. 8: 1067–1077.
3. Gomeni, R., Bani, M., D’Angeli, C., Corsi, M. & Bye, A. (2001). Computer-assisted drug development (CADD): an emerging technology for designing first-time-in-man and proof-of-concept studies from preclin-ical experiments. Eur J Pharm Sci. 13: 261–270.
4. Veselovsky, A. V. & Ivanov, A. S. (2003). Strategy of computer-aided drug design. Curr Drug Targets Infect Disord. 3: 33–40.
5. Stahura, F. L. & Bajorath, J. (2004). Virtual screening methods that complement HTS. Comb Chem High Throughput Screen. 7: 259–269.
6. Guner, O., Clement, O. & Kurogi, Y. (2004). Pharmacophore modeling and three dimensional database searching for drug design using catalyst: Recent advances. Curr Med Chem. 11: 2991–3005.
7. Hansch, C., Leo, A., Mekapati, S. B. & Kurup, A. (2004). Qsar and Adme. Bioorg Med Chem. 12: 3391–3400.
8. Parvu, L. (2003). QSAR – a piece of drug design. J Cell Mol Med. 7: 333–335.
9. Langer, T. & Wolber, G. (2004). Virtual combinatorial chemistry and in silico screening: Efficient tools for lead structure discovery? Pure App Chem. 76: 991–996.
10. Dror, O., Shulman-Peleg, A., Nussinov, R. & Wolfson, H. J. (2004). Predicting molecular interactions in silico: I. A guide to pharmacophore identification and its applications to drug design. Curr Med Chem. 11: 71–90.
11. Perkins, R., Fang, H., Tong, W. D. & Welsh, W. J. (2003). Quantitative structure-activity relationship methods: Perspectives on drug discovery and toxicology. Environ Toxicol Chem. 22: 1666–1679.
12. Tropsha, A. & Zhang, W. F. (2001). Identification of the descriptor pharma-cophores using variable selection QSAR: Applications to database mining. Curr Pharm Design. 7: 599–612.
13. Leo, A. J. & Hansch, C. (1999). Role of hydrophobic effects in mechanistic QSAR. Perspect Drug Discov Design. 17: 1–25.
14. Garg, R., Kurup, A., Mekapati, S. B. & Hansch, C. (2003). Searching for allosteric effects via QSAR. Part II. Bioorg Med Chem. 11: 621–628.
15. Kier, L. B. & Hall, L. H. (1993). The generation of molecular-structures from a graph-based Qsar equation. Quant Struct Act Relat. 12: 383–388.
16. Hall, L. H. & Kier, L. B. (2001). Issues in representation of molecular structure – The development of molecular connectivity. J Mol Graph Model. 20: 4–18.
17. Anker, L. S., Jurs, P. C. & Edwards, P. A. (1990). Quantitative structure retention relationship studies of odor-active aliphatic-compounds with oxygen-containing functional-groups. Anal Chem. 62: 2676–2684.
18. Crippen, G. M. (1982). Distance geometry analysis of the benzodiazepine binding-site. Mol Pharmacol. 22: 11–19.
19. Hopfinger, A. J. (1980). A Qsar Investigation of dihydrofolate-reductase inhibition by baker triazines based upon molecular shape-analysis. J Am Chem Soc. 102: 7196–7206.
20. Boulu, L. G. & Crippen, G. M. (1989). Voronoi binding-site models – calculation of binding modes and influence of drug-binding data accuracy. J Comb Chem. 10: 673–682.
21. Cramer, R. D., III, Patterson, D. E. & Bunce, J. D. (1988). Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc. 110: 5959–5967.
22. So, S. S. & Richards, W. G. (1992). Application of neural networks – quantitative structure-activity-relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl)pyrimi-dines as Dhfr inhibitors. J Med Chem. 35: 3201–3207.
23. Tetko, I. V., Tanchuk, V. Y., Chentsova, N. P., Antonenko, S. V., Poda, G. I., Kukhar, V. P. & Luik, A. I. (1994). Hiv-1 reverse-tran-scriptase inhibitor design using artificial neural networks. J Med Chem. 37: 2520–2526.
24. Ajay, A. & Murcko, M. A. (1995). Computational methods to predict binding free energy in ligand-receptor complexes. J Med Chem. 38: 4953–4967.
25. Andrea, T. A. & Kalayeh, H. (1991). Applications of neural networks in quantitative structure-activity-relationships of dihydrofolate-reductase inhibitors. J Med Chem. 34: 2824–2836.
26. Bolis, G., Dipace, L. & Fabrocini, F. (1991). A machine learning approach to computer-aided molecular design. J Comput Aided Mol Des. 5: 617–628.
27. King, R. D., Muggleton, S., Lewis, R. A. & Sternberg, M. J. E. (1992). Drug design by machine learning – the use of inductive logic programming to model the structure-activity-relationships of trimethoprim analogs binding to dihydrofolate-reductase. Proc Natl Acad Sci U S A. 89: 11322–11326.
28. Jain, A. N., Dietterich, T. G., Lathrop, R. H., Chapman, D., Critchlow, R. E., Bauer, B. E., Webster, T. A. & Lozanoperez, T. (1994). Compass – a shape-based machine learning tool for drug design. J Comput Aided Mol Des. 8: 635–652.
29. Zheng, W. F. & Tropsha, A. (2000). Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle. J Chem Inf Comput Sci. 40: 185–194.
30. Xue, C. X., Zhang, R. S., Liu, H. X., Yao, X. J., Liu, M. C., Hu, Z. D. & Fan, B. T. (2004). An accurate QSPR study of O-H bond dissociation energy in substituted phenols based on support vector machines. J Chem Inf Comput Sci. 44: 669–677.
31. Yao, X. J., Panaye, A., Doucet, J. P., Zhang, R. S., Chen, H. F., Liu, M. C., Hu, Z. D. & Fan, B. T. (2004). Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression. J Chem Inf Comput Sci. 44: 1257–1266.
32. Kovatcheva, A., Golbraikh, A., Oloff, S., Xiao, Y. D., Zheng, W. F., Wolschann, P., Buchbauer, G. & Tropsha, A. (2004). Combinatorial QSAR of ambergris fragrance compounds. J Chem Inf Comput Sci. 44: 582–595.
33. Mjolsness, E. & DeCoste, D. (2001). Machine learning for science: State of the art and future prospects. Science 293: 2051–2055.
34. Herbrich, R. & Williamson, R. C. (2003). Algorithmic luckiness. J Mac Learn Res. 3: 175–212.
35. Schneider, G. & Downs, G. (2003). Machine learning methods in QSAR modelling. QSAR Comb Sci. 22: 485–486.
36. Sebastiani, P., Kohane, I. S. & Ramoni, M. F. (2003). Machine learning in the Genomics era – Editorial: Methods in functional genomics. Machine Learning 52: 5–9.
37. Smith, M. G. & Bull, L. (2003). Feature construction and selection using Genetic Programming and a Genetic Algorithm. Genetic Programming, Proceedings 2610, 229–237.
38. Armengol, E. & Plaza, E. (2003). Discovery of toxicological patterns with lazy learning. Knowledge-Based Intellignet Information and Engineering Systems, Pt 2, Proceedings 2774, 919–926.
39. Oloff, S., Zhang, S., Sukumar, N., Breneman, C. & Tropsha, A. (2006). Chemometric analysis of ligand receptor complementarity: identifying complementary ligands based on receptor information (CoLiBRI). J Chem Inf Model. 46: 844–851.
40. Zhang, S., Golbraikh, A. & Tropsha, A. (2006). Development of quantitative struc-ture-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces. J Med Chem. 49: 2713–2724.
41. Zhang, S., Golbraikh, A., Oloff, S., Kohn, H. & Tropsha, A. (2006). A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J Chem Inf Model 46: 1984–1995.
42. Zhang, S., Wei, L., Bastow, K., Zheng, W., Brossi, A., Lee, K. H. & Tropsha, A. (2007). Antitumor agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents. J Comput Aided Mol Des. 21: 97–112.
43. Duch, W., Swaminathan, K. & Meller, J. (2007). Artificial intelligence approaches for rational drug design and discovery. Curr Pharm Des. 13: 1497–1508.
44. Clark, D. E. & Grootenhuis, P. D. (2002). Progress in computational methods for the prediction of ADMET properties. Curr Opin Drug Discov Dev. 5: 382–390.
45. Davis, A. M. & Riley, R. J. (2004). Predictive ADMET studies, the challenges and the opportunities. Curr Opin Chem Biol. 8: 378–386.
46. Li, H., Yap, C. W., Ung, C. Y., Xue, Y., Li, Z. R., Han, L. Y., Lin, H. H. & Chen, Y. Z. (2007). Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. J Pharm Sci. 96: 2838–2860.
47. Yap, C. W., Cai, C. Z., Xue, Y. & Chen, Y. Z. (2004). Prediction of torsade-causing potential of drugs by support vector machine approach. Toxicol Sci. 79: 170–177.
48. Kubinyi, H. (2003). Drug research: myths, hype and reality. Nat Rev Drug Discov. 2: 665–668.
49. Reddy, M. R. & Erion, M. D. (1998). Structure-based drug design approaches for predicting binding affinities of HIV1 protease inhibitors. J Enzyme Inhib. 14: 1–14.
50. Taylor, R. D., Jewsbury, P. J. & Essex, J. W. (2002). A review of protein-small molecule docking methods. J Comput Aided Mol Des. 16: 151–166.
51. Kuntz, I. D., Meng, E. C. & Shoichet, B. K. (1994). Structure-based molecular design. Acc Chem Res. 27: 117–123.
52. Hardy, L. W. & Malikayil, A. (2003). The impact of structure-guided drug design on clinical agents. Curr Drug Discov. 3: 15–20.
53. Maryanoff, B. E. (2004). Inhibitors of serine proteases as potential therapeutic agents: The road from thrombin to tryptase to cathepsin G. J Med Chem. 47: 769–787.
54. Chen, L. S., Nowak, B. J., Ayres, M. L., Krett, N. L., Rosen, S. T., Zhang, S. & Gandhi, V. (2009). Inhibition of ATP synthase by chlorinated adenosine analogue. Biochem Pharmacol. 78: 583–591.
55. Du-Cuny, L., Song, Z., Moses, S., Powis, G., Mash, E. A., Meuillet, E. J. & Zhang, S. (2009). Computational modeling of novel inhibitors targeting the Akt pleckstrin homology domain. Bioorg Med Chem. 17: 6983–6992.
56. Mahadevan, D., Powis, G., Mash, E. A., et al. (2008). Discovery of a novel class of AKT pleckstrin homology domain inhibitors. Mol Cancer Ther. 7: 2621–2632.
57. Moses, S. A., Ali, M. A., Zuohe, S., Du-Cuny, L., Zhou, L. L., Lemos, R., Ihle, N., Skillman, A. G., Zhang, S., Mash, E. A., Powis, G., Meuillet, E. J. (2009). In vitro and in vivo activity of novel small-molecule inhibitors targeting the pleckstrin homology domain of protein kinase B/AKT. Cancer Res. 69: 5073–5081.
58. Zhang, S., Ying, W. S., Siahaan, T. J. & Jois, S. D. S. (2003). Solution structure of a peptide derived from the beta subunit of LFA-1. Peptides. 24: 827–835.
59. Zhang, S., Kumar, K., Jiang, X., Wallqvist, A. & Reifman, J. (2008). DOVIS: an implementation for high-throughput virtual screening using AutoDock. BMC Bioinformatics 9: 126.
60. Zhang, S., Kaplan, A. H. & Tropsha, A. (2008). HIV-1 protease function and structure studies with the simplicial neighborhood analysis of protein packing method. Proteins. 73: 742–753.
61. Zhang, S. & Du-Cuny, L. (2009). Development and evaluation of a new statistical model for structure-based high-throughput virtual screening. Int J Bioinform Res Appl. 5: 269–279.
62. Kitchen, D. B., Decornez, H., Furr, J. R. & Bajorath, J. (2004). Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat Rev Drug Discov. 3: 935–949.
63. Morris, G. M., Goodsell, D. S., Halliday, R. S., Huey, R., Hart, W. E., Belew, R. K. & Olson, A. J. (1999). Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem. 19: 1639–1662.
64. Halperin, I., Ma, B., Wolfson, H. & Nussinov, R. (2002). Principles of docking: An overview of search algorithms and a guide to scoring functions. Proteins 47: 409–443.
65. Makino, S. & Kuntz, I. D. (1997). Automated flexible ligand docking method and its application for database search. J Comb Chem. 18: 1812–1825.
66. Shoichet, B. K. & Kuntz, I. D. (1991). Protein docking and complementarity. J Mol Biol. 221: 327–346.
67. Kramer, B., Metz, G., Rarey, M. & Lengauer, T. (1999). Ligand docking and screening with FlexX. Med Chem Res. 9: 463–478.
68. Rarey, M., Kramer, B., Lengauer, T. & Klebe, G. (1996). A fast flexible docking method using an incremental construction algorithm. J Mol Biol. 261: 470–489.
69. Goodsell, D. S., Morris, G. M. & Olson, A. J. (1996). Automated docking of flexible ligands: applications of AutoDock. J Mol Recognit. 9: 1–5.
70. Cornell, W. D., Cieplak, P., Bayly, C. I., et al. (1996). A second generation force field for the simulation of proteins, nucleic acids and organic molecules. J Am Chem Soc. 117: 5179–5197.
71. MacKerell, A. D., Jr., Banavali, N. & Foloppe, N. (2000). Development and current status of the CHARMM force field for nucleic acids. Biopolymers. 56: 257–265.
72. Halgren, T. A. (1996). Merck molecular force field: 1. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem. 17: 490–519.
73. Shoichet, B. K., Leach, A. R. & Kuntz, I. D. (1999). Ligand solvation in molecular docking. Proteins. 34: 4–16.
74. Bohm, H. J. (1992). Ludi – rule-based automatic design of new substituents for enzyme-inhibitor leads. J Comput Aided Mol Des. 6: 593–606.
75. Bohm, H. J. (1992). The computer-program Ludi – a new method for the denovo design of enzyme-inhibitors. J Comput Aided Mol Des. 6: 61–78.
76. Bohm, H. J. (1998). Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. J Comput Aided Mol Des. 12: 309–323.
77. Head, R. D., Smythe, M. L., Oprea, T. I., Waller, C. L., Green, S. M. & Marshall, G. R. (1996). VALIDATE: a new method for the receptor-based prediction of binding affinities of novel ligands. J Am Chem Soc. 118: 3959–3969.
78. Gohlke, H., Hendlich, M. & Klebe, G. (2000). Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol. 295: 337–356.
79. DeWitte, R. S. & Shakhnovich, E. I. (1996). SMoG: de novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidence. J Am Chem Soc. 118: 11733–11744.
80. Muegge, I. & Martin, Y. C. (1999). A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem. 42: 791–804.
81. Mitchell, J. B. O., Laskowski, R. A., Alex, A. & Thornton, J. M. (1999). BLEEP-potential of mean force describing protein-ligand interactions: I. Generating potential. J Comput Chem. 20: 1165–1176.
82. Johnston, W. E., Jacobson, V. L., Loken, S. C., Robertson, D. W. & Tierney, B. L. (1992). High-performance computing, high-speed networks, and configurable computing environments: progress toward fully distributed computing. Crit Rev Biomed Eng. 20: 315–354.
83. Golbraikh, A., Shen, M., Xiao, Z. Y., Xiao, Y. D., Lee, K. H. & Tropsha, A. (2003). Rational selection of training and test sets for the development of validated QSAR models. J Comput Aided Mol Des. 17: 241–253.