Journal of Computer-Aided Molecular Design

, Volume 26, Issue 1, pp 115–120 | Cite as

Designing the molecular future

Perspective

Abstract

Approximately 25 years ago the first computer applications were conceived for the purpose of automated ‘de novo’ drug design, prominent pioneering tools being ALADDIN, CAVEAT, GENOA, and DYLOMMS. Many of these early concepts were enabled by innovative techniques for ligand-receptor interaction modeling like GRID, MCSS, DOCK, and CoMFA, which still provide the theoretical framework for several more recently developed molecular design algorithms. After a first wave of software tools and groundbreaking applications in the 1990s—expressly GROW, GrowMol, LEGEND, and LUDI representing some of the key players—we are currently witnessing a renewed strong interest in this field. Innovative ideas for both receptor and ligand-based drug design have recently been published. We here provide a personal perspective on the evolution of de novo design, highlighting some of the historic achievements as well as possible future developments of this exciting field of research, which combines multiple scientific disciplines and is, like few other areas in chemistry, subject to continuous enthusiastic discussion and compassionate dispute.

Keywords

Drug design Computational chemistry Fragment-based design De novo design 

References

  1. 1.
    Gund P, Wipke WT, Langridge R (1974) Computer searching of a molecular structure file for pharmacophoric patterns. Comput Chem Res Educ Technol 3:5–21Google Scholar
  2. 2.
    Martin YC, Bures MG, Willett P (1990) Searching databases of three-dimensional structures. In: Lipkowitz K, Boyd D (eds) Reviews in computational chemistry, vol 1. VCH, Weinheim, pp 213–263CrossRefGoogle Scholar
  3. 3.
    Sheridan RP, Venkataraghavan R (1987) Designing novel nicotinic agonists by searching a database of molecular shapes. J Comput Aided Mol Des 1:243–256CrossRefGoogle Scholar
  4. 4.
    Lewis RA, Dean PM (1989) Automated site-directed drug design: the formation of molecular templates in primary structure generation. Proc R Soc Lond B 236:141–162CrossRefGoogle Scholar
  5. 5.
    VanDrie JH, Weininger D, Martin YC (1989) ALADDIN: an integrated tool for computer-assisted molecular design and pharmacophore recognition from geometric, steric, and substructure searching of three-dimensional molecular structures. J Comput Aided Mol Des 3:225–240CrossRefGoogle Scholar
  6. 6.
    Bartlett PA, Shea GT, Telfer SJ, Waterman S (1989) CAVEAT: a program to facilitate the structure-derived design of biologically active molecules. In: Roberts SM (ed) Molecular recognition in chemical and biological problems, vol 78. Spec Publ R Soc Chem, Cambridge, pp 182–196Google Scholar
  7. 7.
    Lauri G, Bartlett PA (1994) CAVEAT: a program to facilitate the design of organic molecules. J Comput Aided Mol Des 8:51–66CrossRefGoogle Scholar
  8. 8.
    Carhart RE, Smith DH, Gray NAB, Nourse JG, Djerassi C (1981) GENOA: a computer program for structure elucidation utilizing overlapping and alternative substructures. J Org Chem 46:1708–1718CrossRefGoogle Scholar
  9. 9.
    Wise M, Cramer RD, Smith D, Exman I (1983) Progress in three-dimensional drug design: the use of real time color graphics and computer postulation of bioactive molecules in DYLOMMS. In: Dearden JC (ed) Quantitative approaches to drug design. Elsevier, Amsterdam, pp 145–146Google Scholar
  10. 10.
    Goodford PJ (1995) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857CrossRefGoogle Scholar
  11. 11.
    Miranker A, Karplus M (1991) Functionality maps of binding sites: a multiple copy simultaneous search method. Proteins 11:29–34CrossRefGoogle Scholar
  12. 12.
    Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288CrossRefGoogle Scholar
  13. 13.
    Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967CrossRefGoogle Scholar
  14. 14.
    Jackson RC (1995) Update on computer-aided drug design. Curr Opin Biotechnol 6:646–651CrossRefGoogle Scholar
  15. 15.
    Böhm HJ (1996) Current computational tools for de novo ligand design. Curr Opin Biotechnol 7:433–436CrossRefGoogle Scholar
  16. 16.
    Bohacek RS, McMartin C (1997) Modern computational chemistry and drug discovery: structure generating programs. Curr Opin Chem Biol 1:157–161CrossRefGoogle Scholar
  17. 17.
    Marrone TJ, Briggs JM, McCammon JA (1997) Structure-based drug design: computational advances. Annu Rev Pharmacol Toxicol 37:71–90CrossRefGoogle Scholar
  18. 18.
    Kubinyi H (1998) Combinatorial and computational approaches in structure-based drug design. Curr Opin Drug Discov Devel 1:16–27Google Scholar
  19. 19.
    Moon JB, Howe WJ (1991) Computer design of bioactive molecules: a method for receptor-based de novo ligand design. Proteins 11:314–328CrossRefGoogle Scholar
  20. 20.
    Bohacek RS, McMartin C (1994) Multiple highly diverse structures complementary to enzyme binding sites: results of extensive application of a de novo design method incorporating combinatorial growth. J Am Chem Soc 116:5560–5571CrossRefGoogle Scholar
  21. 21.
    Nishibata Y, Itai A (1991) Automatic creation of drug candidate structures based on receptor structure. Starting point for artificial lead generation. Tetrahedron 47:8985–8990CrossRefGoogle Scholar
  22. 22.
    Nishibata Y, Itai A (1993) Confirmation of usefulness of a structure construction program based on three-dimensional receptor structure for rational lead generation. J Med Chem 36:2921–2928CrossRefGoogle Scholar
  23. 23.
    Böhm HJ (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 6:61–78CrossRefGoogle Scholar
  24. 24.
    Böhm HJ (1992) LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J Comput Aided Mol Des 6:593–606CrossRefGoogle Scholar
  25. 25.
    Loving K, Alberts I, Sherman W (2010) Computational approaches for fragment-based and de novo design. Curr Top Med Chem 10:14–32CrossRefGoogle Scholar
  26. 26.
    Hartenfeller M, Schneider G (2011) Enabling future drug discovery by de novo design. WIREs Comp Mol Sci 1:742–759CrossRefGoogle Scholar
  27. 27.
    Schneider G, Fechner U (2005) Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 4:649–663CrossRefGoogle Scholar
  28. 28.
    Reutlinger M, Guba W, Martin RE, Alanine AI, Hoffmann T, Klenner A, Hiss JA, Schneider P, Schneider G (2011) Neighborhood-preserving visualization of adaptive structure-activity landscapes: application to drug discovery. Angew Chem Int Ed Engl. doi:10.1002/anie.201105156
  29. 29.
    Schneider G, Hartenfeller M, Reutlinger M, Tanrikulu Y, Proschak E, Schneider P (2009) Voyages to the (un)known: adaptive design of bioactive compounds. Trends Biotechnol 27:18–26CrossRefGoogle Scholar
  30. 30.
    Dimova D, Wawer M, Wassermann AM, Bajorath J (2011) Design of multitarget activity landscapes that capture hierarchical activity cliff distributions. J Chem Inf Model 51:258–266CrossRefGoogle Scholar
  31. 31.
    Beddell CR, Goodford PJ, Norrington FE, Wilkinson S, Wootton R (1976) Compounds designed to fit a site of known structure in human haemoglobin. Br J Pharmacol 57:201–209Google Scholar
  32. 32.
    Beddell CR, Goodford PJ, Stammers DK, Wootton R (1979) Species differences in the binding of compounds designed to fit a site of known structure in adult human haemoglobin. Br J Pharmacol 65:535–543Google Scholar
  33. 33.
    Lewell XQ, Judd DB, Watson SP, Hann MM (1998) RECAP—retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. J Chem Inf Comput Sci 38:511–522CrossRefGoogle Scholar
  34. 34.
    Vinkers HM, de Jonge MR, Daeyaert FF, Heeres J, Koymans LM, van Lenthe JH, Lewi PJ, Timmerman H, Van Aken K, Janssen PA (2003) SYNOPSIS: synthesize and optimize system in silico. J Med Chem 46:2765–2773CrossRefGoogle Scholar
  35. 35.
    Schneider G, Lee ML, Stahl M, Schneider P (2000) De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks. J Comput Aided Mol Des 14:487–494CrossRefGoogle Scholar
  36. 36.
    Wang R, Gao Y, Lai L (2000) LigBuilder: a multi-purpose program for structure-based drug design. J Mol Model 6:498–516CrossRefGoogle Scholar
  37. 37.
    Kandil S, Biondaro S, Vlachakis D, Cummins AC, Coluccia A, Berry C, Leyssen P, Neyts J, Brancale A (2009) Discovery of a novel HCV helicase inhibitor by a de novo drug design approach. Bioorg Med Chem Lett 19:2935–2937CrossRefGoogle Scholar
  38. 38.
    Feher M, Gao Y, Baber JC, Shirley WA, Saunders J (2008) The use of ligand-based de novo design for scaffold hopping and sidechain optimization: two case studies. Bioorg Med Chem 16:422–427CrossRefGoogle Scholar
  39. 39.
    Lanier MC, Feher M, Ashweek NJ, Loweth CJ, Rueter JK, Slee DH, Williams JP, Zhu YF, Sullivan SK, Brown MS (2007) Selection, synthesis, and structure-activity relationship of tetrahydropyrido[4, 3-d]pyrimidine-2, 4-diones as human GnRH receptor antagonists. Bioorg Med Chem 15:5590–5603CrossRefGoogle Scholar
  40. 40.
    Rogers-Evans M, Alanine A, Bleicher K, Kube D, Schneider G (2004) Identification of novel cannabinoid receptor ligands via evolutionary de novo design and rapid parallel synthesis. QSAR Comb Sci 26:426–430CrossRefGoogle Scholar
  41. 41.
    Schneider G, Neidhart W, Giller T, Schmid G (1999) ‘Scaffold-hopping’ by topological pharmacophore search: a contribution to virtual screening. Angew Chem Int Ed Engl 38:2894–2896CrossRefGoogle Scholar
  42. 42.
    Alig L, Alsenz J, Andjelkovic M, Bendels S, Bénardeau A, Bleicher K, Bourson A, David-Pierson P, Guba W, Hildbrand S, Kube D, Lübbers T, Mayweg AV, Narquizian R, Neidhart W, Nettekoven M, Plancher JM, Rocha C, Rogers-Evans M, Röver S, Schneider G, Taylor S, Waldmeier P (2008) Benzodioxoles: novel cannabinoid-1 receptor inverse agonists for the treatment of obesity. J Med Chem 51:2115–2127CrossRefGoogle Scholar
  43. 43.
    Bissantz C, Kuhn B, Stahl M (2010) A medicinal chemist’s guide to molecular interactions. J Med Chem 53:5061–5084CrossRefGoogle Scholar
  44. 44.
    Mauser H, Guba W (2008) Recent developments in de novo design and scaffold hopping. Curr Opin Drug Discov Devel 11:365–374Google Scholar
  45. 45.
    Langdon SR, Ertl P, Brown N (2010) Bioisosteric replacement and scaffold hopping in lead generation and optimization. Mol Inf 29:366–385CrossRefGoogle Scholar
  46. 46.
    Bailey D, Brown D (2001) High-throughput chemistry and structure-based design: survival of the smartest. Drug Discov Today 6:57–59CrossRefGoogle Scholar
  47. 47.
    Bleicher KH, Böhm HJ, Müller K, Alanine AI (2003) Hit and lead generation: beyond high-throughput screening. Nat Rev Drug Discov 2:369–378CrossRefGoogle Scholar
  48. 48.
    Schneider G (2010) Virtual screening: an endless staircase? Nat Rev Drug Discov 9:273–276CrossRefGoogle Scholar
  49. 49.
    Krüger BA, Dietrich A, Baringhaus KH, Schneider G (2009) Scaffold-hopping potential of fragment-based de novo design: the chances and limits of variation. Comb Chem High Throughput Screen 12:383–396CrossRefGoogle Scholar
  50. 50.
    Sheridan RP, Rusinko A III, Nilakantan R, Venkataraghavan R (1989) Searching for pharmacophores in large coordinate data bases and its use in drug design. Proc Natl Acad Sci USA 86:8165–8169CrossRefGoogle Scholar
  51. 51.
    Babine RE, Bleckman TM, Kissinger CR, Showalter R, Pelletier LA, Lewis C, Tucker K, Moomaw E, Parge HE, Villafranca JE (1995) Design, synthesis and X-ray crystallographic studies of novel FKBB-12 ligands. Bioorg Med Chem Lett 5:1719–1724CrossRefGoogle Scholar
  52. 52.
    Reich SH, Melnick M, Davies JF II, Appelt K, Lewis KK, Fuhry MA, Pino M, Trippe AJ, Nguyen D, Dawson H, Wu BW, Musick L, Kosa M, Kahil D, Webber S, Gehlhaar DK, Andrada D, Shetty B (1995) Protein structure-based design of potent orally bioavailable, nonpeptide inhibitors of human immunodeficiency virus protease. Proc Natl Acad Sci USA 92:3298–3302CrossRefGoogle Scholar
  53. 53.
    Böhm HJ, Banner DW, Weber L (1999) Combinatorial docking and combinatorial chemistry: design of potent non-peptide thrombin inhibitors. J Comput Aided Mol Des 13:51–56CrossRefGoogle Scholar
  54. 54.
    Ripka AS, Satyshur KA, Bohacek RS, Rich DH (2001) Aspartic protease inhibitors designed from computer-generated templates bind as predicted. Org Lett 3:2309–2312CrossRefGoogle Scholar
  55. 55.
    Böhm HJ, Boehringer M, Bur D, Gmuender H, Huber W, Klaus W, Kostrewa D, Kuehne H, Luebbers T, Meunier-Keller N, Mueller F (2000) Novel inhibitors of DNA gyrase: 3D structure based biased needle screening, hit validation by biophysical methods, and 3D guided optimization. A promising alternative to random screening. J Med Chem 43:2664–2674CrossRefGoogle Scholar
  56. 56.
    Schneider G, Clément-Chomienne O, Hilfiger L, Schneider P, Kirsch S, Böhm HJ, Neidhart W (2000) Virtual screening for bioactive molecules by evolutionary de novo design. Angew Chem Int Ed Engl 39:4130–4133Google Scholar
  57. 57.
    Honma T, Hayashi K, Aoyama T, Hashimoto N, Machida T, Fukasawa K, Iwama T, Ikeura C, Ikuta M, Suzuki-Takahashi I, Iwasawa Y, Hayama T, Nishimura S, Morishima H (2001) Structure-based generation of a new class of potent Cdk4 inhibitors: new de novo design strategy and library design. J Med Chem 44:4615–4627CrossRefGoogle Scholar
  58. 58.
    Honma T, Yoshizumi T, Hashimoto N, Hayashi K, Kawanishi N, Fukasawa K, Takaki T, Ikeura C, Ikuta M, Suzuki-Takahashi I, Hayama T, Nishimura S, Morishima H (2001) A novel approach for the development of selective Cdk4 inhibitors: library design based on locations of Cdk4 specific amino acid residues. J Med Chem 44:4628–4640CrossRefGoogle Scholar
  59. 59.
    Ji H, Zhang W, Zhang M, Kudo M, Aoyama Y, Yoshida Y, Sheng C, Song Y, Yang S, Zhou Y, Lü J, Zhu J (2003) Structure-based de novo design, synthesis, and biological evaluation of non-azole inhibitors specific for lanosterol 14alpha-demethylase of fungi. J Med Chem 46:474–485CrossRefGoogle Scholar
  60. 60.
    Liebeschuetz JW, Jones SD, Morgan PJ, Murray CW, Rimmer AD, Roscoe JM, Waszkowycz B, Welsh PM, Wylie WA, Young SC, Martin H, Mahler J, Brady L, Wilkinson K (2002) PRO_SELECT: combining structure-based drug design and array-based chemistry for rapid lead discovery. 2. The development of a series of highly potent and selective factor Xa inhibitors. J Med Chem 45:1221–1232CrossRefGoogle Scholar
  61. 61.
    Pierce AC, Rao G, Bemis GW (2004) BREED: generating novel inhibitors through hybridization of known ligands application to CDK2, P38, and HIV protease. J Med Chem 47:2768–2775CrossRefGoogle Scholar
  62. 62.
    Davies M, Heikkilä T, McConkey GA, Fishwick CW, Parsons MR, Johnson AP (2009) Structure-based design, synthesis, and characterization of inhibitors of human and Plasmodium falciparum dihydroorotate dehydrogenases. J Med Chem 52:2683–2693CrossRefGoogle Scholar
  63. 63.
    Jorgensen WL, Ruiz-Caro J, Tirado-Rives J, Basavapathruni A, Anderson KS, Hamilton AD (2006) Computer-aided design of non-nucleoside inhibitors of HIV-1 reverse transcriptase. Bioorg Med Chem Lett 16:663–667CrossRefGoogle Scholar
  64. 64.
    Schüller A, Suhartono M, Fechner U, Tanrikulu Y, Breitung S, Scheffer U, Göbel MW, Schneider G (2008) The concept of template-based de novo design from drug-derived molecular fragments and its application to TAR RNA. J Comput Aided Mol Des 22:59–68CrossRefGoogle Scholar
  65. 65.
    Firth-Clark S, Kirton SB, Willems HM, Williams A (2008) De novo ligand design to partially flexible active sites: application of the ReFlex algorithm to carboxypeptidase A, acetylcholinesterase, and the estrogen receptor. J Chem Inf Model 48:296–305CrossRefGoogle Scholar
  66. 66.
    Park H, Bahn YJ, Ryu SE (2009) Structure-based de novo design and biochemical evaluation of novel Cdc25 phosphatase inhibitors. Bioorg Med Chem Lett 19:4330–4334CrossRefGoogle Scholar
  67. 67.
    Schneider G, Geppert T, Hartenfeller M, Reisen F, Klenner A, Reutlinger M, Hähnke V, Hiss JA, Zettl H, Keppner S, Spänkuch B, Schneider P (2011) Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors. Future Med Chem 3:415–424CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical SciencesSwiss Federal Institute of Technology (ETH)ZurichSwitzerland

Personalised recommendations