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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References
Klebe, G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discov. Today 11, 580–594 (2006).
Mauser, H. & Guba, W. Recent developments in de novo design and scaffold hopping. Curr. Opin. Drug Discov. Devel. 11, 365–374 (2008).
Köppen, H. Virtual screening: what does it give us? Curr. Opin. Drug Discov. Devel. 12, 397–407 (2009).
Song, C. M., Lim, S. J. & Tong, J. C. Recent advances in computer-aided drug design. Brief. Bioinform. 10, 579–591 (2009).
Jorgensen, W. L. Efficient drug lead discovery and optimization. Acc. Chem. Res. 42, 724–733 (2009).
Böhm, H.-J. & Schneider, G. (eds) Virtual Screening for Bioactive Molecules (Wiley, Weinheim, Germany, 2000).
Alvarez, J. & Shoichet, B. (eds) Virtual Screening in Drug Discovery (CRC Press, Boca Raton, Florida, USA, 2005).
Varnek, A. & Tropsha, A. (eds) Cheminformatics Approaches to Virtual Screening (Royal Society of Chemistry, Cambridge, UK, 2008).
McInnes, C. Virtual screening strategies in drug discovery. Curr. Opin. Chem. Biol. 11, 494–502 (2007).
Bongard, M. M. Pattern Recognition 186–188 (Spartan Books, New York, 1970) [Originally published as Problema Uznavaniya (Nauka Press, Moscow, 1967)].
Dean, P. M. Recent advances in drug design methods: where will they lead? Bioessays 16, 683–687 (1994).
Ellman, J., Stoddard, B. & Wells, J. Combinatorial thinking in chemistry and biology. Proc. Natl Acad. Sci. USA 94, 2779–2782 (1997).
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).
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).
Kortagere, S., Krasowski, M. D. & Ekins, S. The importance of discerning shape in molecular pharmacology. Trends Pharmacol. Sci. 30, 138–147 (2009).
Bredel, M. & Jacoby, E. Chemogenomics: an emerging strategy for rapid target and drug discovery. Nature Rev. Genet. 5, 262–275 (2004).
Kubinyi, H. Chemogenomics in drug discovery. Ernst Schering Res. Found. Workshop 58, 1–19 (2006).
Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nature Chem. Biol. 4, 682–690 (2008).
Wong, C. F. & McCammon, A. J. Protein simulation and drug design. Adv. Protein Chem. 66, 87–121 (2003).
Gilson, M. K. & Zhou, H. X. Calculation of protein–ligand binding affinities. Annu. Rev. Biophys. Biomol. Struct. 36, 21–42 (2007).
Freire, E. Do enthalpy and entropy distinguish first in class from best in class? Drug Discov. Today 13, 869–874 (2008).
Totrov, M. & Abagyan, R. Flexible ligand docking to multiple receptor conformations: a practical alternative. Curr. Opin. Struct. Biol. 18, 178–184 (2008).
B-Rao, C. Subramanian, J. & Sharma, S. D. Managing protein flexibility in docking and its applications. Drug Discov. Today. 14, 394–400 (2009).
Sotriffer, C. A., Sanschagrin, P., Matter, H. & Klebe, G. SFCscore: scoring functions for affinity prediction of protein–ligand complexes. Proteins 73, 395–419 (2008).
Tame, J. R. Scoring functions — the first 100 years. J. Comput. Aided Mol. Des. 19, 445–451 (2005).
Whitesides, G. M. & Krishnamurthy, V. M. Designing ligands to bind proteins. Quart. Rev. Biophys. 38, 385–395 (2005).
Shaw, D. E. et al. Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51, 91–97 (2008).
Claus, B. L. & Johnson, S. R. Grid computing in large pharmaceutical molecular modeling. Drug Discov. Today 13, 578–583 (2008).
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).
Schmidt, M. & Lipson, H. Distilling free-form natural laws from experimental data. Science 324, 81–85 (2009).
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).
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).
Melville, J. L., Burke, E. K. & Hirst, J. D. Machine learning in virtual screening. Comb. Chem. High Throughput Screen. 12, 332–343 (2009).
Koza, J. R. Genetic Programming — On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, Massachussetts, USA, 1992).
Koza, J. R. Genetic Programming II — Automatic Discovery of Reusable Programs (MIT Press, Cambridge, Massachussetts, USA, 1994).
Fechner, U. & Schneider, G. Computer-based de novo design of drug-like molecules. Nature Rev. Drug Discov. 4, 649–663 (2005).
Schneider, G. et al. Voyages to the (un)known: adaptive design of bioactive compounds. Trends Biotechnol. 27, 18–26 (2009).
Hutter, M. C. In silico prediction of drug properties. Curr. Med. Chem. 16, 189–202 (2009).
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).
Schnecke, V. & Boström, J. Computational chemistry-driven decision making in lead generation. Drug Discov. Today 11, 43–50 (2006).
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).
Muegge, I. Synergies of virtual screening approaches. Mini Rev. Med. Chem. 8, 927–933 (2008).
Tanrikulu, Y. & Schneider, G. Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening. Nature Rev. Drug Discov. 7, 667–677 (2008).
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).
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).
Nicholls, A. What do we know and when do we know it? J. Comput. Aided Mol. Des. 22, 239–255 (2008).
Jain, A. N. & Nicholls, A. Recommendations for evaluation of computational methods. J. Comput. Aided Mol. Des. 22, 133–139 (2008).
Irwin, J. J. Community benchmarks for virtual screening. J. Comput. Aided Mol. Des. 22, 193–199 (2008).
Tropsha, A. & Golbraikh, A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr. Pharm. Des. 13, 3494–3504 (2007).
Seifert, M. H. & Lang M. Essential factors for successful virtual screening. Mini Rev. Med. Chem. 8, 63–72 (2008).
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).
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).
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).
Harvey, A. L. Natural products in drug discovery. Drug Discov. Today 13, 894–901 (2008).
Grabowski, K., Baringhaus, K.-H. & Schneider, G. Scaffold diversity of natural products: inspiration for combinatorial library design. Nat. Prod. Rep. 25, 892–904 (2008).
Rollinger, J. M., Stuppner, H. & Langer, T. Virtual screening for the discovery of bioactive natural products. Prog. Drug Res. 65, 213–249 (2009).
Kaiser, M., Wetzel, S., Kumar, K. & Waldmann, H. Biology-inspired synthesis of compound libraries. Cell. Mol. Life Sci. 65, 1186–1201 (2008).
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).
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).
Whiting, M. et al. Inhibitors of HIV-1 protease by using in situ click chemistry. Angew. Chem. Int. Ed. Engl. 45, 1435–1439 (2006).
Schreiber, S. L. The small molecule approach to biology. Chem. Eng. News 81, 51–61 (2003).
Fergus, S., Bender, A. & Spring, D. B. Assessment of structural diversity in combinatorial synthesis. Curr. Opin. Chem. Biol. 9, 304–309 (2005).
Li, J. W. & Vederas, J. C. Drug discovery and natural products: end of an era or an endless frontier? Science 325, 161–165 (2009).
Davey, S. Chemistry: thinking outside the flask. Nature 458, 294 (2009).
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).
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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DOI: https://doi.org/10.1038/nrd3139
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