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Virtual screening

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Virtual screening (VS) is a computational technique used in drug discovery research. It involves the rapid in silico assessment of large libraries of chemical structures in order to identify those structures most likely to bind to a drug target, typically a proteinreceptor or enzyme.[1][2]

Virtual screening has become an integral part of the drug discovery process. Related to the more general and long pursued concept of database searching, the term "virtual screening" is relatively new. Walters, et al. define virtual screening as "automatically evaluating very large libraries of compounds" using computer programs.[3] As this definition suggests, VS has largely been a numbers game focusing on questions like how can we filter down the enormous chemical space of over 1060 conceivable compounds[citation needed] to a manageable number that can be synthesized, purchased, and tested. Although filtering the entire chemical universe might be a fascinating question, 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.

The purpose of virtual screening to come up with hits 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 many hits. Interpretations of VS accuracy should therefore be considered with caution. Low hit rates of interesting scaffolds are clearly preferable over high hit rates of already known scaffolds.

Contents

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[edit]Method

There are two broad categories of screening techniques: ligand-based and structure-based.[4]

[edit]Ligand-based

Given a set of structurally diverse ligands that binds to a receptor, a model of the receptor can be built based on what binds to it. 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.[5]

Another approach to ligand-based virtual screening is to use chemical similarity analysis methods to scan a database of molecules against one active ligand structure.

[edit]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.[6][7]

[edit]Computing Infrastructure

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.

[edit]See also

[edit]References

  1. ^ "From virtuality to reality - Virtual screening in lead discovery and lead optimization: A medicinal chemistry perspective". Curr Opin Drug Discov Devel 11 (4): 559–68. July 2008. PMID 18600572.
  2. ^ Rollinger JM, Stuppner H, Langer T (2008). "Virtual screening for the discovery of bioactive natural products". Prog Drug Res 65 (211): 213–49.doi:10.1007/978-3-7643-8117-2_6PMID 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. ^ McInnes C (2007). "Virtual screening strategies in drug discovery". Curr Opin Chem Biol 11 (5): 494–502. doi:10.1016/j.cbpa.2007.08.033PMID 17936059.
  5. ^ Sun H (2008). "Pharmacophore-based virtual screening". Curr Med Chem 15 (10): 1018–24. doi:10.2174/092986708784049630PMID 18393859.
  6. ^ Kroemer RT (2007). "Structure-based drug design: docking and scoring". Curr Protein Pept Sci 8 (4): 312–28. doi:10.2174/138920307781369382.PMID 17696866.
  7. ^ Cavasotto CN, Orry AJ (2007). "Ligand docking and structure-based virtual screening in drug discovery". Curr Top Med Chem 7 (10): 1006–14.doi:10.2174/156802607780906753PMID 17508934.

[edit]Further reading

  • Melagraki G, Afantitis A, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2007). "Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening". J. Comput. Aided Mol. Des. 21 (5): 251–67. doi:10.1007/s10822-007-9112-4PMID 17377847.
  • Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (2006). "Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques". J. Comput. Aided Mol. Des. 20(2): 83–95. doi:10.1007/s10822-006-9038-2PMID 16783600.
  • Eckert H, Bajorath J (2007). "Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches". Drug Discov. Today 12(5-6): 225–33. doi:10.1016/j.drudis.2007.01.011PMID 17331887.
  • Willett P (2006). "Similarity-based virtual screening using 2D fingerprints". Drug Discov. Today 11 (23-24): 1046–53.doi:10.1016/j.drudis.2006.10.005PMID 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.

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