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Virtual screening

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Virtual screening (VS) is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme.[1][2]

Virtual screening has been defined as the "automatically evaluating very large libraries of compounds" using computer programs.[3] As this definition suggests, VS has largely been a numbers game focusing on how the enormous chemical space of over 1060 conceivable compounds[4] can be filtered to a manageable number that can be synthesized, purchased, and tested. Although searching the entire chemical universe may be a theoretically interesting problem, more practical VS scenarios focus on designing and optimizing targeted combinatorial libraries and enriching libraries of available compounds from in-house compound repositories or vendor offerings. As the accuracy of the method has increased, virtual screening has become an integral part of the drug discovery process.[5]

Methods

There are two broad categories of screening techniques: ligand-based and structure-based.[6]

Ligand-based

Given a set of structurally diverse ligands that binds to a receptor, a model of the receptor can be built by exploiting the collective information contained in such set of ligands. These are known as pharmacophore models. A candidate ligand can then be compared to the pharmacophore model to determine whether it is compatible with it and therefore likely to bind.[7]

Another approach to ligand-based virtual screening is to use 2D chemical similarity analysis methods[8] to scan a database of molecules against one or more active ligand structure.

A popular approach to ligand-based virtual screening is based on searching molecules with shape similar to that of known actives, as such molecules will fit the target's binding site and hence will be likely to bind the target. There are a number of prospective applications of this class of techniques in the literature.[9][10] Pharmacophoric extensions of these 3D methods are also freely-available as webservers.[11][12]

Structure-based

Structure-based virtual screening involves docking of candidate ligands into a protein target followed by applying a scoring function to estimate the likelihood that the ligand will bind to the protein with high affinity.[13][14][15] Webservers oriented to prospective virtual screening are available to all.[16][17]

Computing Infrastructure

The computation of pair-wise interactions between atoms, which is a prerequisite for the operation of many virtual screening programs, is of computational complexity, where N is the number of atoms in the system. Because of the quadratic scaling with respect to the number of atoms, the computing infrastructure may vary from a laptop computer for a ligand-based method to a mainframe for a structure-based method.

Ligand-based

Ligand-based methods typically require a fraction of a second for a single structure comparison operation. A single CPU is enough to perform a large screening within hours. However, several comparisons can be made in parallel in order to expedite the processing of a large database of compounds.

Structure-based

The size of the task requires a parallel computing infrastructure, such as a cluster of Linux systems, running a batch queue processor to handle the work, such as Sun Grid Engine or Torque PBS.

A means of handling the input from large compound libraries is needed. This requires a form of compound database that can be queried by the parallel cluster, delivering compounds in parallel to the various compute nodes. Commercial database engines may be too ponderous, and a high speed indexing engine, such as Berkeley DB, may be a better choice. Furthermore, it may not be efficient to run one comparison per job, because the ramp up time of the cluster nodes could easily outstrip the amount of useful work. To work around this, it is necessary to process batches of compounds in each cluster job, aggregating the results into some kind of log file. A secondary process, to mine the log files and extract high scoring candidates, can then be run after the whole experiment has been run.

Accuracy

The aim of virtual screening is to identify molecules of novel chemical structure that bind to the macromolecular target of interest. Thus, success of a virtual screen is defined in terms of finding interesting new scaffolds rather than the total number of hits. Interpretations of virtual screening accuracy should therefore be considered with caution. Low hit rates of interesting scaffolds are clearly preferable over high hit rates of already known scaffolds.

Most tests of virtual screening studies in the literature are retrospective. In these studies, the performance of a VS technique is measured by its ability to retrieve a small set of previously known molecules with affinity to the target of interest (active molecules or just actives) from a library containing a much higher proportion of assumed inactives or decoys. By contrast, in prospective applications of virtual screening, the resulting hits are subjected to experimental confirmation (e.g., IC50 measurements). There is consensus that retrospective benchmarks are not good predictors of prospective performance and consequently only prospective studies constitute conclusive proof of the suitability of a technique for a particular target.[18][19][20][21]

See also

References

  1. ^ Rester U (July 2008). "From virtuality to reality - Virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective". Current Opinion in Drug Discovery & Development. 11 (4): 559–68. PMID 18600572.
  2. ^ Rollinger JM, Stuppner H, Langer T (2008). "Virtual screening for the discovery of bioactive natural products". Progress in Drug Research. Progress in Drug Research. 65 (211): 211, 213–49. doi:10.1007/978-3-7643-8117-2_6. ISBN 978-3-7643-8098-4. PMID 18084917.
  3. ^ Walters WP, Stahl MT, Murcko MA (1998). "Virtual screening – an overview". Drug Discov. Today. 3 (4): 160–178. doi:10.1016/S1359-6446(97)01163-X.
  4. ^ Bohacek RS, McMartin C, Guida WC (1996). "The art and practice of structure-based drug design: a molecular modeling perspective". Med. Res. Rev. 16: 3–50. doi:10.1002/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6.
  5. ^ McGregor, Malcolm J; Luo, Zhaowen; Jiang, Xuliang (June 11, 2007). "Chapter 3: Virtual screening in drug discovery". In Huang, Ziwei (ed.). Drug Discovery Research. New Frontiers in the Post-Genomic Era. Wiley-VCH: Weinheim, Germany. pp. 63–88. ISBN 978-0-471-67200-5.
  6. ^ McInnes C (October 2007). "Virtual screening strategies in drug discovery". Current Opinion in Chemical Biology. 11 (5): 494–502. doi:10.1016/j.cbpa.2007.08.033. PMID 17936059.
  7. ^ Sun H (2008). "Pharmacophore-based virtual screening". Current Medicinal Chemistry. 15 (10): 1018–24. doi:10.2174/092986708784049630. PMID 18393859.
  8. ^ Willet P, Barnard JM, Downs GM (1998). "Chemical similarity searching". J Chem Inf Comput Sci. 38 (6): 983–996. doi:10.1021/ci9800211.
  9. ^ Rush TS, Grant JA, Mosyak L, Nicholls A (March 2005). "A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction". Journal of Medicinal Chemistry. 48 (5): 1489–95. doi:10.1021/jm040163o. PMID 15743191.
  10. ^ Ballester PJ, Westwood I, Laurieri N, Sim E, Richards WG (February 2010). "Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N-acetyltransferases". Journal of the Royal Society, Interface / the Royal Society. 7 (43): 335–42. doi:10.1098/rsif.2009.0170. PMC 2842611. PMID 19586957.
  11. ^ Li H, Leung KS, Wong MH, Ballester PJ (July 2016). "USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniques". Nucleic Acids Research. 44 (W1): W436-41. doi:10.1093/nar/gkw320. PMID 27106057.
  12. ^ Sperandio O, Petitjean M, Tuffery P (July 2009). "wwLigCSRre: a 3D ligand-based server for hit identification and optimization". Nucleic Acids Research. 37 (Web Server issue): W504-9. doi:10.1093/nar/gkp324. PMC 2703967. PMID 19429687.
  13. ^ Kroemer RT (August 2007). "Structure-based drug design: docking and scoring". Current Protein & Peptide Science. 8 (4): 312–28. doi:10.2174/138920307781369382. PMID 17696866.
  14. ^ Cavasotto CN, Orry AJ (2007). "Ligand docking and structure-based virtual screening in drug discovery". Current Topics in Medicinal Chemistry. 7 (10): 1006–14. doi:10.2174/156802607780906753. PMID 17508934.
  15. ^ Kooistra AJ, Vischer HF, McNaught-Flores D, Leurs R, de Esch IJ, de Graaf C (2016). "Function-specific virtual screening for GPCR ligands using a combined scoring method". Scientific Reports. 6: 28288. doi:10.1038/srep28288. PMID 27339552.
  16. ^ Irwin JJ, Shoichet BK, Mysinger MM, Huang N, Colizzi F, Wassam P, Cao Y (September 2009). "Automated docking screens: a feasibility study". Journal of Medicinal Chemistry. 52 (18): 5712–20. doi:10.1021/jm9006966. PMC 2745826. PMID 19719084.
  17. ^ Li H, Leung KS, Ballester PJ, Wong MH (2014-01-24). "istar: a web platform for large-scale protein-ligand docking". PloS One. 9 (1): e85678. doi:10.1371/journal.pone.0085678. PMC 3901662. PMID 24475049.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  18. ^ Irwin JJ (2008). "Community benchmarks for virtual screening". Journal of Computer-Aided Molecular Design. 22 (3–4): 193–9. doi:10.1007/s10822-008-9189-4. PMID 18273555.
  19. ^ Good AC, Oprea TI (2008). "Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection?". Journal of Computer-Aided Molecular Design. 22 (3–4): 169–78. doi:10.1007/s10822-007-9167-2. PMID 18188508.
  20. ^ Schneider G (April 2010). "Virtual screening: an endless staircase?". Nature Reviews. Drug Discovery. 9 (4): 273–6. doi:10.1038/nrd3139. PMID 20357802.
  21. ^ Ballester PJ (January 2011). "Ultrafast shape recognition: method and applications". Future Medicinal Chemistry. 3 (1): 65–78. doi:10.4155/fmc.10.280. PMID 21428826.

Further reading

  • Melagraki G, Afantitis A, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (May 2007). "Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening". Journal of Computer-Aided Molecular Design. 21 (5): 251–67. doi:10.1007/s10822-007-9112-4. PMID 17377847.
  • Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (February 2006). "Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques". Journal of Computer-Aided Molecular Design. 20 (2): 83–95. doi:10.1007/s10822-006-9038-2. PMID 16783600.
  • Eckert H, Bajorath J (March 2007). "Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches". Drug Discovery Today. 12 (5–6): 225–33. doi:10.1016/j.drudis.2007.01.011. PMID 17331887.
  • Willett P (December 2006). "Similarity-based virtual screening using 2D fingerprints". Drug Discovery Today. 11 (23–24): 1046–53. doi:10.1016/j.drudis.2006.10.005. PMID 17129822.
  • Fara DC, Oprea TI, Prossnitz ER, Bologa CG, Edwards BS, Sklar LA (2006). "Integration of virtual and physical screening". Drug Discov. Today: Technologies. 3 (4): 377–385. doi:10.1016/j.ddtec.2006.11.003.
  • Muegge I, Oloffa S (2006). "Advances in virtual screening". Drug Discov. Today: Technologies. 3 (4): 405–411. doi:10.1016/j.ddtec.2006.12.002.
  • Schneider G (April 2010). "Virtual screening: an endless staircase?". Nature Reviews. Drug Discovery. 9 (4): 273–6. doi:10.1038/nrd3139. PMID 20357802.
  • VLS3D – list of over 2000 databases, online and standalone in silico tools
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