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Fragment-Based Methods in Drug Discovery pp 119–135Cite as

Computational Methods for Fragment-Based Ligand Design: Growing and Linking

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1289))

Abstract

Fragment-based drug design has proved itself as a powerful technique for increasing the sampling and diversity of chemical space and enabling the design of novel leads and compounds. Computational techniques for identifying fragments, binding sites and particularly for linking, growing, and evolving fragments play a significant role in the process. Information from ADME studies and clustering property information in the form of toxicophores and chemotypes can play a significant role in aiding the design of novel, selective fragments with good activity profiles.

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References

  1. Shuker SB, Hajduk PJ, Meadows RP, Fesik SW (1996) Discovering high-affinity ligands for proteins: SAR by NMR. Science 274:1531–1534

    Article  CAS  PubMed  Google Scholar 

  2. Congreve M, Chessari G, Tisi D, Woodhead AJ (2008) Recent developments in fragment-based drug discovery. J Med Chem 51:3661–3680

    Article  CAS  PubMed  Google Scholar 

  3. Mazanetz M, Law R, Whittaker M (2014) Hit and lead Identification from fragments. In: Scheider G (ed) De novo molecular design. Wiley, New York, pp 143–200

    Google Scholar 

  4. Davis BJ, Erlandson DA (2013) Learning from our mistakes: the ‘unknown knowns’ in fragment screening. Bioorg Med Chem Lett 23:2844–2852

    Article  CAS  PubMed  Google Scholar 

  5. Murray CW, Verdonk ML, Rees DC (2012) Experiences in fragment-based drug discovery. Trends Pharmacol Sci 33:224–232

    Article  CAS  PubMed  Google Scholar 

  6. van Deursen R, Blum LC, Reymond JL (2011) Visualization of the chemical space of fragments, lead-like and drug-like molecules in PubChem. J Comput Aided Mol Des 25:649–662

    Article  CAS  PubMed  Google Scholar 

  7. Blum JC, van Deursen R, Reymond JL (2011) Visualisation and subsets of the chemical universe dataset GDB-13 for virtual screening. J Comput Aided Mol Des 25:637–647

    Article  CAS  PubMed  Google Scholar 

  8. Polishchuk PG, Madzhidov TI, Varnek A (2013) Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des 27(8):675–679

    Article  CAS  PubMed  Google Scholar 

  9. Bohm HJ (1992) LUDI: rule based automatic design of new substituents for enzyme inhibitor leads. J Comput Aided Mol Des 6:593–606

    Article  CAS  PubMed  Google Scholar 

  10. Mattos C, Bellamacina CR, Peisach E, Pereira A, Vitkup D, Petsko GA, Ringe D (2006) Multiple solvent crystal structures: probing binding sites, plasticity and hydration. J Mol Biol 357:1471–1482

    Article  CAS  PubMed  Google Scholar 

  11. Raman EP, Yu W, Guvench O, MacKerell AD Jr (2011) Reproducing crystal binding modes of ligand functional groups using site-identification by ligand competitive saturation (SILCS) simulations. J Chem Inf Model 51:877–896

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Ngan CH, Bohnuud T, Mottarella SE, Beglov D, Villar EA, Hall DR, Kozakov D, Vajda S (2012) FTMAP: extended protein mapping with user-selected probe molecules. Nucleic Acids Res 40:W271–W275

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. 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 38:511–522

    Article  CAS  PubMed  Google Scholar 

  14. Cooper T, Andrews-Cramer K (2000) Designed chemical libraries for hit/lead optimization. Innovat Pharm Technol 5:46–53

    Google Scholar 

  15. Cramer RD, Soltanshahi F, Jilek R, Campbell B (2007) AllChem: generating and searching 10(20) synthetically accessible structures. J Comput Aided Mol Des 21:341–350

    Article  CAS  PubMed  Google Scholar 

  16. Rarey M, Dixon JS (1998) Feature trees: a new molecular similarity measure based on tree matching. J Comput Aided Mol Des 12:471–490

    Article  CAS  PubMed  Google Scholar 

  17. Lounkine E, Auer J, Bajorath J (2008) Formal concept analysis for the identification of molecular fragment combinations specific for active and highly potent compounds. J Med Chem 51:5342–5348

    Article  CAS  PubMed  Google Scholar 

  18. Batista J, Godden JW, Bajorath J (2006) Assessment of molecular similarity from the analysis of randomly generated structural fragment populations. J Chem Inf Model 46:1937–1944

    Article  CAS  PubMed  Google Scholar 

  19. Khashan R (2012) FraVLib a free database mining software for generating “Fragment-based Virtual Library” using pocket similarity search of ligand-receptor complexes. J Cheminform 4:18

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. de Graaf C, Vischer HF, de Kloe GE et al (2013) Small and colorful stones make beautiful mosaics: fragment-based chemogenomics. Drug Discov Today 18(7–8):323–330

    Article  PubMed  Google Scholar 

  21. Yuling A, Sherman W, Dixon SL (2012) Hole filling and library optimization: application to commercially available fragment libraries. Bioorg Med Chem 20:5379–5387

    Article  Google Scholar 

  22. Sirci F, Enade P, Istyastono HF et al (2012) Virtual fragment screening: discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints. J Chem Inf Model 52:3308–3324

    Article  CAS  PubMed  Google Scholar 

  23. Favia AD, Bottegoni G, Nobeli I et al (2011) SERAPhiC: a benchmark for in silico fragment-based drug design. J Chem Inf Model 51:2882–2896

    Article  CAS  PubMed  Google Scholar 

  24. Zauhar RJ, Gianti E, Welsh WJ (2013) Fragment-based shape signatures: a new tool for virtual screening and drug discovery. J Comput Aided Mol Des 27:1009–1036

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Hoffer L, Renaud JP, Horvath DJ (2013) In silico fragment-based drug discovery: setup and validation of a fragment-to-lead computational protocol using S4MPLE. J Chem Inf Model 53(4):836–851

    Article  CAS  PubMed  Google Scholar 

  26. Gillet V, Johnson AP, Mata P et al (1993) Sprout: a program for structure generation. J Comput Aided Mol Des 7:127–153

    Article  CAS  PubMed  Google Scholar 

  27. Day F, Caflisch A (2008) Fragment based de novo ligand design by multiobjective evolutionary optimization. J Chem Inf Model 2008(48):679–690

    Article  Google Scholar 

  28. Yuan H, Tai W, Hu S et al (2013) Fragment-based strategy for structural optimization in combination with 3D-QSAR. J Comput Aided Mol Des 27:897–915

    Article  CAS  PubMed  Google Scholar 

  29. Durrant JD, Lindert S, McCammon JA (2013) Autogrow 3.0: an improved algorithm for chemically tractable, semi-automated protein inhibitor design. J Mol Graph Model 44:104–112

    Article  CAS  PubMed  Google Scholar 

  30. Durrant JD, McCammon JA (2012) AutoClickChem: click chemistry in silico. PLoS Comput Biol 8:e1002397

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  31. Pierce AC, RaoG BGW (2004) BREED: generating novel inhibitors through hybridization of known ligands, applications to CDK2, p38 and HIV protease. J Med Chem 47:2768–2775

    Article  CAS  PubMed  Google Scholar 

  32. Lindert S, Durrant JD, McCammon JA (2012) LigMerge: a fast algorithm to generate models of novel potential ligands from sets of known binders. Chem Biol Drug Des 80:358–365

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. Chung S, Parker JB, Bianchet M, Amzel LM, Stivers JT (2009) Impact of linker strain and flexibility in the design of a fragment-based inhibitor. Nat Chem Biol 5:407–413

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  34. Thompson DC, Denny RA, Nilakantan R et al (2008) CONFIRM: connecting fragments found in receptor molecules. J Comput Aided Mol Des 22:761–772

    Article  CAS  PubMed  Google Scholar 

  35. Maass P, Schulz-Gasch T, Stahl M, Rarey M (2007) Recore: a fast and versatile method for scaffold hopping based on small molecule crystal structure conformations. J Chem Inf Model 47:390–399

    Article  CAS  PubMed  Google Scholar 

  36. Vainio MJ, Kogej T, Raubacher F, Sadowski J (2013) Scaffold hopping by fragment replacement. J Chem Inf Model 53:1825–1835

    Article  CAS  PubMed  Google Scholar 

  37. Kump JL III, Blumentahl SN, Wang Q et al (2012) A fragment-based approach to the SAMPL3 Challenge. J Comput Aided Mol Des 26:583–594

    Article  Google Scholar 

  38. Kazius J, McGuire R, Bursi R R (2005) Derivation and validation of toxicophores for mutagenicity prediction. J Med Chem 48:312–320

    Article  CAS  PubMed  Google Scholar 

  39. von Korff M, Sander T T (2006) Toxicity-indicating structural patterns. J Chem Inf Model 46:536–544

    Article  Google Scholar 

  40. Ahmed J, Worth CL, Thaben P et al (2011) Fragment Store—a comprehensive database of fragments linking metabolites, toxic molecules and drugs. Nucleic Acids Res 39:D1049–D1054

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Kaneshisa M (2002) The KEGG database. Novartis Found Symp 247:91–101

    Article  Google Scholar 

  42. Wisharte DS, Knos C, Guo AC et al (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36:D901–D906

    Article  Google Scholar 

  43. Schmidt U, Struck S, Gruening B et al (2009) SuperToxic: a comprehensive database of toxic compounds. Nucleic Acids Res 37:D295–D299

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  44. Roberts G, Myatt GJ, Johnson WP et al (2000) Leadscope: software for exploring large sets of screening data. J Chem Inf Comput Sci 40:1302–1314

    Article  CAS  PubMed  Google Scholar 

  45. Erlanson DA (2012) Introduction to fragment based drug discovery. Top Curr Chem 317:1–32

    Article  CAS  PubMed  Google Scholar 

  46. Bollag G, Tsai J, Zhang J et al (2012) Vemurafenib: the first drug approved for BRAF-mutant cancer. Nat Rev Drug Discov 11:873–886

    Article  CAS  PubMed  Google Scholar 

  47. Rougley SD, Hubbard RE (2011) More novel computational fragment based methods will facilitate further drug design in the future. J Med Chem 54:3989–4005

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Independent Researcher and Consultant, 300 Pitch Pine Lane, Chapel Hill, NC, 27514, USA

    Rachelle J. Bienstock

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  1. Rachelle J. Bienstock
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Corresponding author

Correspondence to Rachelle J. Bienstock .

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Editors and Affiliations

  1. Pennsylvania Drug Discovery Institute, Doylestown, Pennsylvania, USA

    Anthony E. Klon

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Bienstock, R.J. (2015). Computational Methods for Fragment-Based Ligand Design: Growing and Linking. In: Klon, A. (eds) Fragment-Based Methods in Drug Discovery. Methods in Molecular Biology, vol 1289. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2486-8_10

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  • DOI: https://doi.org/10.1007/978-1-4939-2486-8_10

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2485-1

  • Online ISBN: 978-1-4939-2486-8

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