Virtual screening: Difference between revisions
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Revision as of 10:19, 24 January 2009
It has been suggested that Virtual high throughput screening and Talk:Virtual screening#Merge proposal be merged into this article. (Discuss) Proposed since July 2008. |
Virtual screening ("VS") has become an integral part of the drug discovery process in recent years. 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.[1] 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 main goal of a virtual screen is to come up with hits of novel chemical structure that yield a unique pharmacological profile. 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.
See also
References
- ^ 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.
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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.
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: CS1 maint: multiple names: authors list (link) - 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.
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: CS1 maint: multiple names: authors list (link) - 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.
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: CS1 maint: multiple names: authors list (link) - 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.
External links
- Free on-line virtual_screening service
- 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