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| '''Virtual screening''' (VS) is a computational technique used in [[drug discovery]] to search libraries of [[small molecule]]s in order to identify those structures which are most likely to bind to a [[drug target]], typically a [[protein]] [[receptor (biochemistry)|receptor]] or [[enzyme]].<ref name="pmid18600572">{{cite journal | author = | title = From virtuality to reality - Virtual screening in lead discovery and lead optimization: A medicinal chemistry perspective | journal = Curr Opin Drug Discov Devel | volume = 11 | issue = 4 | pages = 559–68 |date=July 2008 | pmid = 18600572| last1 = Rester | first1 = U }}</ref><ref name="pmid18084917">{{cite journal | author = Rollinger JM, Stuppner H, Langer T | title = Virtual screening for the discovery of bioactive natural products | journal = Prog Drug Res | volume = 65 | issue = 211 | pages = 213–49 | year = 2008 | pmid = 18084917| doi = 10.1007/978-3-7643-8117-2_6 | series = Progress in Drug Research | isbn = 978-3-7643-8098-4 }}</ref>
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| Virtual screening has been defined as the "automatically evaluating very large libraries of compounds" using computer programs.<ref name="Walters_1998">{{cite journal | author = Walters WP, Stahl MT, Murcko MA | title = Virtual screening – an overview | journal =Drug Discov. Today | volume = 3 | issue = 4 | pages = 160–178 | year = 1998| doi = 10.1016/S1359-6446(97)01163-X }}</ref> As this definition suggests, VS has largely been a numbers game focusing on how the enormous [[chemical space]] of over 10<sup>60</sup> conceivable compounds<ref name="Bohacek_1996">{{cite journal | author = Bohacek RS, McMartin C, Guida WC | title = The art and practice of structure-based drug design: a molecular modeling perspective | journal = Med. Res. Rev. | volume = 16 | pages = 3–50 | year = 1996| doi = 10.1002/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6 }}</ref> 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.<ref name="VSDD_2007">{{cite book |last1=McGregor |first1=Malcolm J |last2=Luo |first2=Zhaowen |last3=Jiang |first3=Xuliang |editor-last=Huang |editor-first=Ziwei |title= Drug Discovery Research. New Frontiers in the Post-Genomic Era |publisher=Wiley-VCH: Weinheim, Germany |date=June 11, 2007 |pages=63-88 |chapter=Chapter 3: Virtual screening in drug discovery |isbn=978-0-471-67200-5}}</ref>
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| == Methods ==
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| There are two broad categories of screening techniques: ligand-based and structure-based.<ref name="pmid17936059">{{cite journal | author = McInnes C | title = Virtual screening strategies in drug discovery | journal = Curr Opin Chem Biol | volume = 11 | issue = 5 | pages = 494–502 | year = 2007| pmid = 17936059 | doi = 10.1016/j.cbpa.2007.08.033 }}</ref>
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| === Ligand-based ===
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| Given a set of structurally diverse [[ligands]] that binds to a [[receptor (biochemistry)|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.<ref name="pmid18393859">{{cite journal | author = Sun H | title = Pharmacophore-based virtual screening | journal = Curr Med Chem | volume = 15 | issue = 10 | pages = 1018–24 | year = 2008 | pmid = 18393859| doi = 10.2174/092986708784049630 }}</ref>
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| A different strategy is to develop logic-based rules describing features of substructures and chemical properties related to activity using support vector inductive logic programming.<ref name ="Reynolds">{{cite journal | author = Reynolds CR, Amini AC, Muggleton SH, Sternberg MJ | title = Assessment of a Rule-Based Virtual Screening Technology (INDDEx) on a Benchmark Data Set | journal = J Phys Chem | year = 2012 | volume = 116 | issue = 23| pages = 6732–6739 | doi = 10.1021/jp212084f}}</ref> The logic-based features provide insights into activity which can be understood by medicinal chemists. Support vector machine integrate the features to yield a quantitative QSAR, which is then used to screen a database of molecules. This approach is well suited to scaffold hopping to identify novel active molecules and is implemented in the package [http://www.equinoxpharma.com/ INDDEx].
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| Another approach to ligand-based virtual screening is to use 2D chemical similarity analysis methods<ref name ="Willet1998">{{cite journal | author = Willet P, Barnard JM, Downs GM | title = Chemical similarity searching | journal = J Chem Inf Comput Sci | year = 1998 | volume = 38 | issue = 6 | pages = 983–996 | doi = 10.1021/ci9800211}}</ref> to scan a database of molecules against one or more active ligand structure.
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| 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.<ref name ="Rush_2005">{{cite journal | author = Rush TS, Grant JA, Mosyak L, Nicholls A | title = A Shape-Based 3-D Scaffold Hopping Method and Its Application to a Bacterial Protein−Protein Interaction | journal = Journal of Medicinal Chemistry | year = 2005 | volume = 48 | pages = 1489–1495 | doi = 10.1021/jm040163o}}</ref><ref name ="Ballester_2010">{{cite journal | author = Ballester PJ, Westwood I, Laurieri N, Sim E, Richards WG | title = Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N-acetyltransferases | journal = Journal of The Royal Society Interface | year = 2010 | volume = 7 | pages = 335–342 | pmid = 19586957 | doi=10.1098/rsif.2009.0170 | issue=43 | pmc=2842611}}</ref>
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| === Structure-based ===
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| Structure-based virtual screening involves [[docking (molecular)|docking]] of candidate ligands into a protein target followed by applying a [[scoring functions for docking|scoring function]] to estimate the likelihood that the ligand will bind to the protein with high affinity.<ref name="pmid17696866">{{cite journal | author = Kroemer RT | title = Structure-based drug design: docking and scoring | journal = Curr Protein Pept Sci | volume = 8 | issue = 4 | pages = 312–28 | year = 2007 | pmid = 17696866| doi = 10.2174/138920307781369382 }}</ref><ref name="pmid17508934">{{cite journal | author = Cavasotto CN, Orry AJ | title = Ligand docking and structure-based virtual screening in drug discovery | journal = Curr Top Med Chem | volume = 7 | issue = 10 | pages = 1006–14 | year = 2007 | pmid = 17508934 | doi = 10.2174/156802607780906753 }}</ref>
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| == Computing Infrastructure ==
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| The computation of pair-wise interactions between atoms, which is a prerequisite for the operation of many virtual screening programs, is of <math>O(N^{2})</math> 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.
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| === Ligand-based ===
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| 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.
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| === Structure-based ===
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| 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.
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| 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.
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| == Accuracy ==
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| The aim of virtual screening is to identify molecules of novel chemical structure that bind to the macromolecular [[biological target|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 rate]]s of interesting scaffolds are clearly preferable over high hit rates of already known scaffolds.
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| 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|IC<sub>50</sub>]] 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.<ref name="Irwin_2008">{{cite journal | author = Irwin J | title = Community benchmarks for virtual screening | journal = Journal of Computer-Aided Molecular Design | volume = 22 | issue = 3-4 | pages = 193–199 | year = 2008| doi = 10.1007/s10822-008-9189-4}}</ref><ref name="Good_2008">{{cite journal | author = Good AC, Oprea TI | title = Optimization of CAMD techniques 3. Virtual screening enrichment studies: a help or hindrance in tool selection? | journal = Journal of Computer-Aided Molecular Design | volume = 22 | issue = 3-4 | pages = 169–78 | year = 2008| doi = 10.1007/s10822-007-9167-2}}</ref><ref name="Schneider_2010">{{cite journal | author = Schneider G | title = Virtual screening: an endless staircase? | journal = Nature Reviews Drug Discovery | volume = 9 | pages = 273–276 | year = 2010 | doi = 10.1038/nrd3139 | pmid=20357802}}</ref><ref name="Ballester_2011">{{cite journal | author = Ballester PJ | title = Ultrafast shape recognition: method and applications | journal = Future Medicinal Chemistry | volume = 3 | issue = 1 | pages = 65–78 | year = 2011 | doi = 10.4155/fmc.10.280}}</ref>
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| == See also ==
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| * [[High-throughput screening]]
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| * [[Drug discovery]]
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| * [[Docking (molecular)]]
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| * [[Scoring functions for docking|Scoring functions]]
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| * [[ZINC database]]
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| ==References==
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| {{Reflist|35em}}
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| == Further reading ==
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| {{refbegin|35em}}
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| * {{cite journal | author = Melagraki G, Afantitis A, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O | title = Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening | journal = J. Comput. Aided Mol. Des. | volume = 21 | issue = 5 | pages = 251–67 | year = 2007 | pmid = 17377847 | doi = 10.1007/s10822-007-9112-4 }}
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| * {{cite journal | author = Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O | title = Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques | journal = J. Comput. Aided Mol. Des. | volume = 20 | issue = 2 | pages = 83–95 | year = 2006 | pmid = 16783600 | doi = 10.1007/s10822-006-9038-2 }}
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| * {{cite journal | author = Eckert H, Bajorath J | title = Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches | journal = Drug Discov. Today | volume = 12 | issue = 5–6 | pages = 225–33 | year = 2007 | pmid = 17331887 | doi = 10.1016/j.drudis.2007.01.011 }}
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| * {{cite journal | author = Willett P | title = Similarity-based virtual screening using 2D fingerprints | journal = Drug Discov. Today | volume = 11 | issue = 23–24 | pages = 1046–53 | year = 2006 | pmid = 17129822 | doi = 10.1016/j.drudis.2006.10.005 }}
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| * {{cite journal | author = Fara DC, Oprea TI, Prossnitz ER, Bologa CG, Edwards BS, Sklar LA | title = Integration of virtual and physical screening | journal = Drug Discov. Today: Technologies | volume = 3 | issue = 4 | pages = 377–385 | year = 2006| doi = 10.1016/j.ddtec.2006.11.003 }}
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| * {{cite journal | author = Muegge I, Oloffa S | title = Advances in virtual screening | journal = Drug Discov. Today: Technologies | volume = 3 | issue = 4 | pages = 405–411 | year = 2006| doi = 10.1016/j.ddtec.2006.12.002 }}
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| * {{cite journal | author = Schneider G | title = Virtual screening: an endless staircase? | journal = Nat Rev Drug Discov | volume = 9 | issue = 4 | pages = 273–6 |date=April 2010 | pmid = 20357802 | doi = 10.1038/nrd3139 }}
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| {{refend}}
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| == External links ==
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| * [http://www.schrodinger.com/productpage/14/5/ Glide] — ligand-receptor docking and virtual screening software
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| * [http://www.equinoxpharma.com/ INDDEx] — ligand-based hit discovery for scaffold hopping using SVILP machine learning
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| * [http://blaster.docking.org/zinc/ ZINC] — a free database of commercially-available compounds for virtual screening.
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| * [http://barryhardy.blogs.com/cheminfostream/2006/09/virtual_screeni.html Virtual Screening Methods]
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| * [https://mcule.com/ mcule] — Easy-to-use, online virtual screening.
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| * [http://www.molinspiration.com/cgi-bin/properties Free service to screen for GPCR ligands, ion channel blockers and kinase inhibitors]
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| * [http://www.visipoint.fi/brutus.php Brutus] — a similarity analysis tool for ligand-based virtual screening.
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| * [http://www.novamechanics.com NovaMechanics Cheminformatics Research] Combined structure & ligand based chemistry driven virtual screening.
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| [[Category:Bioinformatics]]
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| [[Category:Drug discovery]]
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| [[Category:Cheminformatics]]
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| [[Category:Alternatives to animal testing]]
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