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Virtual screening: an endless staircase?

Abstract

Computational chemistry — in particular, virtual screening — can provide valuable contributions in hit- and lead-compound discovery. Numerous software tools have been developed for this purpose. However, despite the applicability of virtual screening technology being well established, it seems that there are relatively few examples of drug discovery projects in which virtual screening has been the key contributor. Has virtual screening reached its peak? If not, what aspects are limiting its potential at present, and how can significant progress be made in the future?

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Figure 1: Virtual screening today?

References

  1. 1

    Klebe, G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov. Today 11, 580–594 (2006).

    CAS  Article  Google Scholar 

  2. 2

    Mauser, H. & Guba, W. Recent developments in de novo design and scaffold hopping. Curr. Opin. Drug Discov. Devel. 11, 365–374 (2008).

    CAS  PubMed  Google Scholar 

  3. 3

    Köppen, H. Virtual screening: what does it give us? Curr. Opin. Drug Discov. Devel. 12, 397–407 (2009).

    PubMed  Google Scholar 

  4. 4

    Song, C. M., Lim, S. J. & Tong, J. C. Recent advances in computer-aided drug design. Brief. Bioinform. 10, 579–591 (2009).

    CAS  Article  Google Scholar 

  5. 5

    Jorgensen, W. L. Efficient drug lead discovery and optimization. Acc. Chem. Res. 42, 724–733 (2009).

    CAS  Article  Google Scholar 

  6. 6

    Böhm, H.-J. & Schneider, G. (eds) Virtual Screening for Bioactive Molecules (Wiley, Weinheim, Germany, 2000).

    Book  Google Scholar 

  7. 7

    Alvarez, J. & Shoichet, B. (eds) Virtual Screening in Drug Discovery (CRC Press, Boca Raton, Florida, USA, 2005).

    Book  Google Scholar 

  8. 8

    Varnek, A. & Tropsha, A. (eds) Cheminformatics Approaches to Virtual Screening (Royal Society of Chemistry, Cambridge, UK, 2008).

    Book  Google Scholar 

  9. 9

    McInnes, C. Virtual screening strategies in drug discovery. Curr. Opin. Chem. Biol. 11, 494–502 (2007).

    CAS  Article  Google Scholar 

  10. 10

    Bongard, M. M. Pattern Recognition 186–188 (Spartan Books, New York, 1970) [Originally published as Problema Uznavaniya (Nauka Press, Moscow, 1967)].

    Google Scholar 

  11. 11

    Dean, P. M. Recent advances in drug design methods: where will they lead? Bioessays 16, 683–687 (1994).

    CAS  Article  Google Scholar 

  12. 12

    Ellman, J., Stoddard, B. & Wells, J. Combinatorial thinking in chemistry and biology. Proc. Natl Acad. Sci. USA 94, 2779–2782 (1997).

    CAS  Article  Google Scholar 

  13. 13

    Ballester, P. J., Westwood, I., Laurieri, N., Sim, E. & Richards, W. G. Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N-acetyltransferases. J. R. Soc. Interface 7, 335–342 (2010).

    CAS  Article  Google Scholar 

  14. 14

    Irwin, J. J. & Shoichet, B. K. ZINC — a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 45, 177–182 (2005).

    CAS  Article  Google Scholar 

  15. 15

    Kortagere, S., Krasowski, M. D. & Ekins, S. The importance of discerning shape in molecular pharmacology. Trends Pharmacol. Sci. 30, 138–147 (2009).

    CAS  Article  Google Scholar 

  16. 16

    Bredel, M. & Jacoby, E. Chemogenomics: an emerging strategy for rapid target and drug discovery. Nature Rev. Genet. 5, 262–275 (2004).

    CAS  Article  Google Scholar 

  17. 17

    Kubinyi, H. Chemogenomics in drug discovery. Ernst Schering Res. Found. Workshop 58, 1–19 (2006).

    CAS  Article  Google Scholar 

  18. 18

    Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nature Chem. Biol. 4, 682–690 (2008).

    CAS  Article  Google Scholar 

  19. 19

    Wong, C. F. & McCammon, A. J. Protein simulation and drug design. Adv. Protein Chem. 66, 87–121 (2003).

    CAS  Article  Google Scholar 

  20. 20

    Gilson, M. K. & Zhou, H. X. Calculation of protein–ligand binding affinities. Annu. Rev. Biophys. Biomol. Struct. 36, 21–42 (2007).

    CAS  Article  Google Scholar 

  21. 21

    Freire, E. Do enthalpy and entropy distinguish first in class from best in class? Drug Discov. Today 13, 869–874 (2008).

    CAS  Article  Google Scholar 

  22. 22

    Totrov, M. & Abagyan, R. Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr. Opin. Struct. Biol. 18, 178–184 (2008).

    CAS  Article  Google Scholar 

  23. 23

    B-Rao, C. Subramanian, J. & Sharma, S. D. Managing protein flexibility in docking and its applications. Drug Discov. Today. 14, 394–400 (2009).

    CAS  Article  Google Scholar 

  24. 24

    Sotriffer, C. A., Sanschagrin, P., Matter, H. & Klebe, G. SFCscore: scoring functions for affinity prediction of protein–ligand complexes. Proteins 73, 395–419 (2008).

    CAS  Article  Google Scholar 

  25. 25

    Tame, J. R. Scoring functions — the first 100 years. J. Comput. Aided Mol. Des. 19, 445–451 (2005).

    CAS  Article  Google Scholar 

  26. 26

    Whitesides, G. M. & Krishnamurthy, V. M. Designing ligands to bind proteins. Quart. Rev. Biophys. 38, 385–395 (2005).

    CAS  Article  Google Scholar 

  27. 27

    Shaw, D. E. et al. Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51, 91–97 (2008).

    Article  Google Scholar 

  28. 28

    Claus, B. L. & Johnson, S. R. Grid computing in large pharmaceutical molecular modeling. Drug Discov. Today 13, 578–583 (2008).

    CAS  Article  Google Scholar 

  29. 29

    Klepeis, J. L., Lindorff-Larsen, K., Dror, R. O. & Shaw, D. E. Long-timescale molecular dynamics simulations of protein structure and function. Curr. Opin. Struct. Biol. 19, 120–127 (2009).

    CAS  Article  Google Scholar 

  30. 30

    Schmidt, M. & Lipson, H. Distilling free-form natural laws from experimental data. Science 324, 81–85 (2009).

    CAS  Article  Google Scholar 

  31. 31

    Schneider, G., Tanrikulu, Y. & Schneider, P. Self-organizing maps in drug discovery: compound library design, scaffold-hopping, repurposing. Curr. Med. Chem. 16, 258–266 (2009).

    CAS  Article  Google Scholar 

  32. 32

    Schwaighofer, A., Schroeter, T., Mika, S. & Blanchard, G. How wrong can we get? A review of machine learning approaches and error bars. Comb. Chem. High Throughput Screen. 12, 453–468 (2009).

    CAS  Article  Google Scholar 

  33. 33

    Melville, J. L., Burke, E. K. & Hirst, J. D. Machine learning in virtual screening. Comb. Chem. High Throughput Screen. 12, 332–343 (2009).

    CAS  Article  Google Scholar 

  34. 34

    Koza, J. R. Genetic Programming — On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, Massachussetts, USA, 1992).

    Google Scholar 

  35. 35

    Koza, J. R. Genetic Programming II — Automatic Discovery of Reusable Programs (MIT Press, Cambridge, Massachussetts, USA, 1994).

    Google Scholar 

  36. 36

    Fechner, U. & Schneider, G. Computer-based de novo design of drug-like molecules. Nature Rev. Drug Discov. 4, 649–663 (2005).

    Article  Google Scholar 

  37. 37

    Schneider, G. et al. Voyages to the (un)known: adaptive design of bioactive compounds. Trends Biotechnol. 27, 18–26 (2009).

    CAS  Article  Google Scholar 

  38. 38

    Hutter, M. C. In silico prediction of drug properties. Curr. Med. Chem. 16, 189–202 (2009).

    CAS  Article  Google Scholar 

  39. 39

    Rester, U. From virtuality to reality — virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective. Curr. Opin. Drug Discov. Devel. 11, 559–568 (2008).

    CAS  PubMed  Google Scholar 

  40. 40

    Schnecke, V. & Boström, J. Computational chemistry-driven decision making in lead generation. Drug Discov. Today 11, 43–50 (2006).

    CAS  Article  Google Scholar 

  41. 41

    Jenwitheesuk, E., Horst, J. A., Rivas, K. L., Van Voorhis, W. C. & Samudrala, R. Novel paradigms for drug discovery: computational multitarget screening. Trends Pharmacol. Sci. 29, 62–71 (2008).

    CAS  Article  Google Scholar 

  42. 42

    Muegge, I. Synergies of virtual screening approaches. Mini Rev. Med. Chem. 8, 927–933 (2008).

    CAS  Article  Google Scholar 

  43. 43

    Tanrikulu, Y. & Schneider, G. Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening. Nature Rev. Drug Discov. 7, 667–677 (2008).

    CAS  Article  Google Scholar 

  44. 44

    Kontoyianni, M., Madhav, P., Suchanek, E. & Seibel, W. Theoretical and practical considerations in virtual screening: a beaten field? Curr. Med. Chem. 15, 107–116 (2008).

    CAS  Article  Google Scholar 

  45. 45

    Reddy, A. S., Pati, S. P., Kumar, P. P., Pradeep, H. N. & Sastry, G. N. Virtual screening in drug discovery — a computational perspective. Curr. Protein Pept. Sci. 8, 329–351 (2007).

    CAS  Article  Google Scholar 

  46. 46

    Nicholls, A. What do we know and when do we know it? J. Comput. Aided Mol. Des. 22, 239–255 (2008).

    CAS  Article  Google Scholar 

  47. 47

    Jain, A. N. & Nicholls, A. Recommendations for evaluation of computational methods. J. Comput. Aided Mol. Des. 22, 133–139 (2008).

    CAS  Article  Google Scholar 

  48. 48

    Irwin, J. J. Community benchmarks for virtual screening. J. Comput. Aided Mol. Des. 22, 193–199 (2008).

    CAS  Article  Google Scholar 

  49. 49

    Tropsha, A. & Golbraikh, A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr. Pharm. Des. 13, 3494–3504 (2007).

    CAS  Article  Google Scholar 

  50. 50

    Seifert, M. H. & Lang M. Essential factors for successful virtual screening. Mini Rev. Med. Chem. 8, 63–72 (2008).

    CAS  Article  Google Scholar 

  51. 51

    Rupp, M., Schneider, P. & Schneider, G. Distance phenomena in high-dimensional chemical descriptor spaces: consequences for similarity-based approaches. J. Comput. Chem. 30, 2285–2296 (2009).

    CAS  PubMed  Google Scholar 

  52. 52

    Guido, R. V., Oliva, G. & Andricopulo, A. D. Virtual screening and its integration with modern drug design technologies. Curr. Med. Chem. 15, 37–46 (2008).

    CAS  Article  Google Scholar 

  53. 53

    Newman, D. J. & Cragg, G. M. Natural products as sources of new drugs over the last 25 years. J. Nat. Prod. 70, 461–477 (2007).

    CAS  Article  Google Scholar 

  54. 54

    Harvey, A. L. Natural products in drug discovery. Drug Discov. Today 13, 894–901 (2008).

    CAS  Article  Google Scholar 

  55. 55

    Grabowski, K., Baringhaus, K.-H. & Schneider, G. Scaffold diversity of natural products: inspiration for combinatorial library design. Nat. Prod. Rep. 25, 892–904 (2008).

    CAS  Article  Google Scholar 

  56. 56

    Rollinger, J. M., Stuppner, H. & Langer, T. Virtual screening for the discovery of bioactive natural products. Prog. Drug Res. 65, 213–249 (2009).

    Google Scholar 

  57. 57

    Kaiser, M., Wetzel, S., Kumar, K. & Waldmann, H. Biology-inspired synthesis of compound libraries. Cell. Mol. Life Sci. 65, 1186–1201 (2008).

    CAS  Article  Google Scholar 

  58. 58

    Burke, M. D., Berger, E. M. & Schreiber, S. L. A synthesis strategy yielding skeletally diverse small molecules combinatorially. J. Am. Chem. Soc. 126, 14095–14104 (2004).

    CAS  Article  Google Scholar 

  59. 59

    Kolb, H. C., Finn, M. G. & Sharpless, K. B. Click chemistry: diverse chemical function from a few good reactions. Angew. Chem. Int. Ed. Engl. 40, 2004–2021 (2001).

    CAS  Article  Google Scholar 

  60. 60

    Whiting, M. et al. Inhibitors of HIV-1 protease by using in situ click chemistry. Angew. Chem. Int. Ed. Engl. 45, 1435–1439 (2006).

    CAS  Article  Google Scholar 

  61. 61

    Schreiber, S. L. The small molecule approach to biology. Chem. Eng. News 81, 51–61 (2003).

    Article  Google Scholar 

  62. 62

    Fergus, S., Bender, A. & Spring, D. B. Assessment of structural diversity in combinatorial synthesis. Curr. Opin. Chem. Biol. 9, 304–309 (2005).

    CAS  Article  Google Scholar 

  63. 63

    Li, J. W. & Vederas, J. C. Drug discovery and natural products: end of an era or an endless frontier? Science 325, 161–165 (2009).

    Article  Google Scholar 

  64. 64

    Davey, S. Chemistry: thinking outside the flask. Nature 458, 294 (2009).

    CAS  Article  Google Scholar 

  65. 65

    Boehm, M., Wu, T. Y., Claussen, H. & Lemmen, C. Similarity searching and scaffold hopping in synthetically accessible combinatorial chemistry spaces. J. Med. Chem. 51, 2468–2480 (2008).

    CAS  Article  Google Scholar 

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Acknowledgements

I am grateful to P. Schneider, J. A. Hiss, Y. Tanrikulu, H. Köppen, and K.-H. Baringhaus for stimulating discussions about myths and facts of virtual screening, and helpful comments on the manuscript.

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Schneider, G. Virtual screening: an endless staircase?. Nat Rev Drug Discov 9, 273–276 (2010). https://doi.org/10.1038/nrd3139

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