Virtual screening
Virtual screening (VS) is a computational technique used in drug discovery research. By using computers, it deals with the quick search of large libraries of chemical structures 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 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[4] 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 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 many of these 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 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[5][6][7][8].
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[edit] Method
There are two broad categories of screening techniques: ligand-based and structure-based.[9]
[edit] 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.[10]
Another approach to ligand-based virtual screening is to use 2D chemical similarity analysis methods[11] 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[12][13].
[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.[14][15]
[edit] Computing Infrastructure
The computation of pair-wise interactions between atoms, which is a prerequisite for the operation of many virtual screening programs, is of O(N2) computational complexity, where N is the number of atoms in the system. Because of the exponential 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.
[edit] 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.
[edit] 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.
[edit] See also
[edit] References
- ^ Rester, U (July 2008). "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. PMID 18600572.
- ^ Rollinger JM, Stuppner H, Langer T (2008). "Virtual screening for the discovery of bioactive natural products". Prog Drug Res. Progress in Drug Research 65 (211): 213–49. doi:10.1007/978-3-7643-8117-2_6. ISBN 978-3-7643-8098-4. PMID 18084917.
- ^ 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.
- ^ 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.
- ^ Irwin J (2008). "Community benchmarks for virtual screening". Journal of Computer-Aided Molecular Design 22 (3-4): 193–199. doi:10.1007/s10822-008-9189-4.
- ^ 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.
- ^ Schneider G (2010). "Virtual screening: an endless staircase?". Nature Reviews Drug Discovery 9: 273–276. doi:10.1038/nrd3139.
- ^ Ballester PJ (2011). "Ultrafast shape recognition: method and applications". Future Medicinal Chemistry 3 (1): 65–78. doi:10.4155/fmc.10.280.
- ^ McInnes C (2007). "Virtual screening strategies in drug discovery". Curr Opin Chem Biol 11 (5): 494–502. doi:10.1016/j.cbpa.2007.08.033. PMID 17936059.
- ^ Sun H (2008). "Pharmacophore-based virtual screening". Curr Med Chem 15 (10): 1018–24. doi:10.2174/092986708784049630. PMID 18393859.
- ^ Willet P, Barnard JM, Downs GM (1998). "Chemical similarity searching". J Chem Inf Comput Sci 38 (6): 983–996. doi:10.1021/ci9800211.
- ^ Rush TS, Grant JA, Mosyak L, Nicholls A (2005). "A Shape-Based 3-D Scaffold Hopping Method and Its Application to a Bacterial Protein−Protein Interaction". Journal of Medicinal Chemistry 48: 1489–1495. doi:10.1021/jm040163o.
- ^ Ballester PJ, Westwood I, Laurieri N, Sim E, Richards WG (2010). "Prospective virtual screening with Ultrafast Shape Recognition: the identification of novel inhibitors of arylamine N-acetyltransferases". Journal of The Royal Society Interface 7: 335–342. PMID 19586957.
- ^ Kroemer RT (2007). "Structure-based drug design: docking and scoring". Curr Protein Pept Sci 8 (4): 312–28. doi:10.2174/138920307781369382. PMID 17696866.
- ^ Cavasotto CN, Orry AJ (2007). "Ligand docking and structure-based virtual screening in drug discovery". Curr Top Med Chem 7 (10): 1006–14. doi:10.2174/156802607780906753. PMID 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/s10822-007-9112-4. PMID 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/s10822-006-9038-2. PMID 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.2007.01.011. PMID 17331887.
- Willett P (2006). "Similarity-based virtual screening using 2D fingerprints". Drug Discov. Today 11 (23–24): 1046–53. doi:10.1016/j.drudis.2006.10.005. 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 (2010). "Virtual screening: an endless staircase?". Nature Reviews Drug Discovery 9: 273–276. doi:10.1038/nrd3139.
[edit] External links
- ZINC — a free database of commercially-available compounds for virtual screening.
- Virtual Screening Methods
- Free service to screen for GPCR ligands, ion channel blockers and kinase inhibitors
- Brutus — a similarity analysis tool for ligand-based virtual screening.
- NovaMechanics Cheminformatics Research Combined structure & ligand based chemistry driven virtual screening.
