Computer-Aided Drug Design: An Innovative Tool for Modeling

Abstract

Strategies for CADD vary depending on the extent of structural and other information available regarding the target (enzyme/receptor) and the ligands. Computer-aided drug design (CADD) is an exciting and diverse discipline where various aspects of applied and basic research merge and stimulate each other. In the early stage of a drug discovery process, researchers may be faced with little or no structure activity relationship (SAR) information. The process by which a new drug is brought to market stage is referred to by a number of names most commonly as the development chain or “pipeline” and consists of a number of distinct stages. To design a rational drug, we must firstly find out which proteins can be the drug targets in pathogenesis. In present review we reported a brief history of CADD, DNA as target, receptor theory, structure optimization, structure-based drug design, virtual high-throughput screening (vHTS), graph machines.

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P. Kore, M. Mutha, R. Antre, R. Oswal and S. Kshirsagar, "Computer-Aided Drug Design: An Innovative Tool for Modeling," Open Journal of Medicinal Chemistry, Vol. 2 No. 4, 2012, pp. 139-148. doi: 10.4236/ojmc.2012.24017.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] W. G. Richards, “Computer-Aided Drug Design,” Pure and Applied Chemistry, Vol. 66, No. 8, 1994, pp. 1589-1596. doi:10.1351/pac199466081589
[2] I. S. Haworth, C. Burt, F. Gago, C. A. Reynolds and W. G. Richards, “A Prototype Bioreductive DNA Groove Binding Ligand,” Anticancer Drug Design, Vol. 6, No. 1, 1991, pp. 59-61.
[3] I. S. Haworth, A. H. Elcock, A. Rodger and W. G. Richards, “Sequence Selective Binding to the DNA Major Groove: Tris (1,lO-phenanhline) Metal Complexes Binding to Poly(dG-dC) and Poly(dA-dT),” Journal of Biomolecular Structure and Dynamics, Vol. 9, No. 1, 1991, pp. 23-29. doi:10.1080/07391102.1991.10507891
[4] I. S. Haworth, A. H. Elcock, A. Rodger and W. G. Richards, “A Binding Mode of A-[tris(l,lO-phenanthrohe)ruthenium(II)]2+ Exhibiting Preference for Purine-3’,5’- Pyrimidine Sites of DNA,” Journal of Biomolecular Structure and Dynamics, Vol. 9, No. 1, 1991, pp. 553-569. doi:10.1080/07391102.1991.10507936
[5] M. Jayakanthan, G. Wadhwa, T. M. Mohan, L. Arul, P. Balasubramanian and D. Sundar, “Computer-Aided Drug Design for Cancer-Causing H-Ras p21 Mutant Protein,” Letters in Drug Design and Discovery, Vol. 6, No. 1, 2009, pp. 14-20. doi:10.2174/157018009787158526
[6] D. A. Spandidos and N. M. Wilkie, “Malignant Transformation of Early Passage Rodent Cells by a Single Mutated Human Oncogene,” Nature, Vol. 310, 1984, pp. 469-475.
[7] A. Valencia, P. Chardin, A. Wittinghofer and C. Sander, “The Ras Protein Family: Evolutionary Tree and Role of Conserved Amino Acids,” Biochemistry, Vol. 30, No. 19, 1991, pp. 4637-4648. doi:10.1021/bi00233a001
[8] M. Barbacid, “Ras Genes,” Annual Review of Biochemistry, Vol. 56, 1987, pp. 779-827. doi:10.1146/annurev.bi.56.070187.004023
[9] R. Khosravi-Far and C. J. Der, “The Ras Signal Transduction Pathway,” Cancer and Metastasis Reviews, Vol. 13, No. 1, 1994, pp. 67-89. doi:10.1007/BF00690419
[10] A. Russo, V. Bazan, V. Agnese, V. Rodolico and N. Gebbia, “Prognostic and Predictive Factors in Colorectal Cancer: Kirsten Ras in CRC (RASCAL) and TP53CRC Collaborative Studies,” Annals of Oncology, Vol. 4, Suppl 4, 2005, pp. 44-49.
[11] G. Oxford and D. Theodorescu, “The Role of Ras Superfamily Proteins in Bladder Cancer Progression,” Journal of Urology, Vol. 170, No. 5, 2003, pp. 1987-1993. doi:10.1097/01.ju.0000088670.02905.78
[12] J. John, M. Frech and A. Wittinghofer, “Biochemical Properties of Haras Encoded p21 Mutants and Mechanism of the Autophosphorylation Reaction,” The Journal of Biological Chemistry, Vol. 263, 1988, pp. 11792-11799.
[13] H. Kiaris and D. A. Spandidos, “Mutations of Ras Genes in Human Tumors,” International Journal of Oncology, Vol. 7, No. 3, 1995, pp. 413-421.
[14] L. George and J. H. Ching, “VPython Application To The Computer-Aided Drug Design Problem,” The Python Papers Monograph, Vol. 2, 2010, pp. 1-5.
[15] Structure-Activity Relationships (QSAR), “A Review, Combinatorial Chemistry & High Throughput Screening,” Vol. 9, 2006, pp. 213-228.
[16] C. D. Selassie, “Burger’s Medicinal Chemistry, Drug Discovery and Development,” 6th Edition, John Wiley & Sons, Inc., New York, 2003.
[17] D. G. Sprous, J. Zhang, L. Zhang, Z. Wang and M. A. Tepper, “Kinase Inhibitor Recognition by Use of a Multivariable QSAR Model,” Journal of Molecular Graphics and Modelling, Vol. 24, No. 4, 2006, pp. 278-295. doi:10.1016/j.jmgm.2005.09.004
[18] F. Ooms, “Molecular Modeling and Computer Aided Drug Design. Examples of their Applications in Medicinal Chemistry,” Current Medicinal Chemistry, Vol. 7, No. 2, 2000, pp. 141-158. doi:10.2174/0929867003375317
[19] J. Kuhlman, International Journal of Clinical Pharmacology and Therapeutics, Vol. 35, 1997, pp. 541-552.
[20] R. G. Halliday, S. R. Walker and C. E. Lumley, Journal of Pharmaceutical Medicine, Vol. 2, 1992, pp. 139-154.
[21] K. R. Oldenburg, “Annual Report in Medicinal Chemistry,” J. A. Bristol, Ed., Academic Press, London, Vol. 33, 1998, pp. 301-307.
[22] W. H. Moos, G. D. Green and M. R. Pavia, “Chapter 33. Recent Advances in the Generation of Molecular Diversity,” Annual Reports in Medicinal Chemistry, Vol. 28, 1993, pp. 315-324. doi:10.1016/S0065-7743(08)60903-3
[23] A. K. Ghose and J. J. Wendoloski, “Perspective in Drug Discovery and Design,” Kluwer/Escom, Vol. 9-11, 1998, pp. 253-271.
[24] D. J. Abraham and G. E. Kellogg, “3D-QSAR in Drug Design,” H. Kubinyi, Ed., Escom, Leiden, Vol. 1, 1993, pp. 506-522.
[25] D. Scherer, P. Dubois and B. Sherwood, “VPython: 3D Interactive Scientific Graphics for Students,” Computing in Science and Engineering, Vol. 2, No. 5, 2000, pp. 56-62.
[26] J. Irwin, D. M. Lorber, S. L. McGovern, B. Wei and B. K. Shoichet, “Docking and Drug Discovery,” Computational Nanoscience and Nanotechnology, Vol. 2, 2002, pp. 50-51.
[27] C. A. Taft, V. B. da Silva and C. H. T de P. da Silva, “Current Topics in Computer-Aided Drug Design,” Journal of Pharmaceutical Sciences, Vol. 97, No. 3, 2008, pp. 1089-1098. doi:10.1002/jps.21293
[28] D. Bernard, A. Coop and A. D. MacKerell Jr., “Computer-Aided Drug Design: Structure-Activity Relationships of Delta Opioid Ligands,” Drug Design Reviews, Vol. 2, No. 4, 2005, pp. 277-291. doi:10.2174/1567269054087596
[29] W. G. Richards, “Computer-Aided Drug Design,” Pure and Applied Chemistry, Vol. 66, No. 8, 1994, pp. 1589-1598. doi:10.1351/pac199466081589
[30] D. S. Park, J. M. Kim, Y. B. Lee and C. H. Ahn, “QSID Tool: A New Three-Dimensional QSAR Environmental Tool,” Journal of Computer-Aided Drug Design, Vol. 22, No. 12, 2008, pp. 873-883.
[31] H. Kubinyi, “Chance Favors the Prepared Mind—From Serendipity to Rational Drug Design,” Journal of Receptor and Signal Transduction Research, Vol. 19, No. 1-4, 1999, pp. 15-39. doi:10.3109/10799899909036635
[32] M. von Itzstein, W. Y. Wu, G. B. Kok, M. S. Pegg, J. C. Dyason, B. Jin, T. V. Phan, M. L. Smythe, H. F. White, S. W. Oliver, P. M. Colman, J. N. Varghese, D. M. Ryan, J. M. Woods, R. C. Bethell, V. J. Hotham, J. M. Cameron and C. R. Penn, “Rational Design of Potent SialidaseBased Inhibitors of Influenza Virus Replication,” Nature, Vol. 363, No. 6428, 1993, pp. 418-423. doi:10.1038/363418a0
[33] J. Greer, J. W. Erickson, J. J. Baldwin and M. D. Varney, “Application of the Three-Dimensional Structures of Protein Target Molecules in Structure-Based Drug Design,” Journal of Medicinal Chemistry, Vol. 37, No. 8, 1994, pp. 1035-1054. doi:10.1021/jm00034a001
[34] J. P. Vacca and J. H. Condra, “Clinically Effective HIV-1 Protease Inhibitors,” Drug Discovery Today, Vol. 2, No. 1, 1997, pp. 6-18. doi:10.1016/S1359-6446(97)01053-2
[35] P. C. Sternweis and A. G. Gilman, “Aluminum: A Requirement for Activation of the Regulatory Component of Adenylate Cyclase by Fluoride,” Proceedings of the National Academy of Sciences USA, Vol. 79, No. 16, 1982, pp. 4888-4891. doi:10.1073/pnas.79.16.4888
[36] J. Sondek, D. G. Lambright, J. P. Noel, H. E. Hamm and P. B. Sigler, “GTPase Mechanism of Gproteins from the 1.7-? Crystal Structure of Transducin α.GDP.AlF4-,” Nature, Vol. 372, 1994, pp. 276-279. doi:10.1038/372276a0
[37] G. De Stevens, “Serendipity and Structured Research in Drug Discovery,” Progress in Drug Research, Vol. 30, 1986, pp. 189-203.
[38] P. J. Goodford, “Drug Design by the Method of Receptor Fit,” Journal of Medicinal Chemistry, Vol. 27, No. 5, 1984, pp. 557-564. doi:10.1021/jm00371a001
[39] J. P. Vacca and J. H. Condra, “Clinically Effective HIV-1 Protease Inhibitors,” Drug Discovery Today, Vol. 2, No. 1, 1997, pp. 6-18. doi:10.1016/S1359-6446(97)01053-2
[40] A. Goulon, A. Duprat and G. Dreyfus, “Graph Machines and Their Applications to Computer-Aided Drug Design: A New Approach to Learning from Structured Data,” Lecture Notes in Computer Science, Vol. 4135, 2006, pp. 1-19.
[41] C. Jochum and J. Gasteiger, “Canonical Numbering and Constitutional Symmetry,” Journal of Chemical Information and Computer Sciences, Vol. 17, No. 2, 1977, pp. 113-117. doi:10.1021/ci60010a014
[42] A. T. Balaban, S. C. Basak, T. Colburn and G. D. Grunwald, “Correlation between Structureand Normal Boiling Points of Haloalkanes C1-C4 Using Neural Networks,” Journal of Chemical Information and Computer Sciences, Vol. 34, No. 5, 1994, pp. 1118-1121. doi:10.1021/ci00021a016
[43] C. Rucker, M. Meringer and A. Kerber, “QSPR Using MOLGEN-QSPR: The Example of Haloalkane Boiling Points,” Journal of Chemical Information and Computer Sciences, Vol. 44, No. 6, 2004, pp. 2070-2076. doi:10.1021/ci049802u
[44] L. Douali, D. Villemin and D. Cherqaoui, “Exploring QSAR of Non-Nucleoside Reverse Transcriptase Inhibitors by Neural Networks: TIBO Derivatives,” International Journal of Molecular Sciences, Vol. 5, No. 2, 2004, pp. 48-55. doi:10.3390/i5020048
[45] J. Huuskonen, “QSAR Modeling with the Electrotopological State, TIBO Derivatives,” Journal of Chemical Information and Computer Sciences, Vol. 41, No. 2, 2001, pp. 425-429. doi:10.1021/ci0001435
[46] Z. Zhou and J. D. Madura, “CoMFA 3D-QSAR Analysis of HIV-1 RT Nonnucleoside Inhibitors, TIBO Derivatives Based on Docking Conformation and Alignment,” Journal of Chemical Information and Computer Sciences, Vol. 44, No. 6, 2004, pp. 2167-2178. doi:10.1021/ci049893v
[47] B. Hammer, “Recurrent Networks for Structured Data— A Unifying Approach and its Properties,” Cognitive Systems Research, Vol. 3, No. 2, 2002, pp. 145-165. doi:10.1016/S1389-0417(01)00056-0
[48] C. Jochum and J. Gasteiger, “Canonical Numbering and Constitutional Symmetry,” Journal of Chemical Information and Computer Sciences, Vol. 17, No. 2, 1977, pp. 113-117. doi:10.1021/ci60010a014
[49] A. T. Balaban, S. C. Basak, T. Colburn and G. D. Grunwald, “Correlation between Structure and Normal Boiling Points of Haloalkanes C1-C4 Using Neural Networks,” Journal of Chemical Information and Computer Sciences, Vol. 34, No. 5, 1994, pp. 1118-1121. doi:10.1021/ci00021a016
[50] C. Rucker, M. Meringer and A. Kerber, “QSPR Using MOLGEN-QSPR: The Example of Haloalkane Boiling Points,” Journal of Chemical Information and Computer Sciences, Vol. 44, No. 6, 2004, pp. 2070-2076. doi:10.1021/ci049802u
[51] J. Augen, “The Evolving Role of Information Technology in the Drug Discovery Process,” Drug Discovery Today, Vol. 7, Suppl 5, 2002, pp. 275-282.
[52] H. Van de waterbeemd and E. Gifford, “ADMET in Silico Modelling, towards Prediction Paradise?” Nature Reviews Drug Discovery, Vol. 2, No. 3, 2003, pp. 192-204. doi:10.1038/nrd1032
[53] W. J. Egan, G. Zlokarnik and P. D. J. Grootenhuis, “In Silico Prediction of Drug Safety: Despite Progress There Is Abundant Room for Improvement,” Drug Discovery Today: Technologies, Vol. 1, Suppl 4, 2004, pp. 381-387.
[54] A. Rostami-Hodjegan and G. Tucker, “‘In Silico’ Simulations to Assess the ‘in Vivo’ Consequences of ‘in Vitro’ Metabolic Drug-Drug Interactions,” Drug Discovery Today: Technologies, Vol. 1, Suppl 4, 2004, pp. 441-448.

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