Nikolova N Jaworska J Approaches to Measure Chemical Similarity - a Review

Chemical term

Chemical similarity (or molecular similarity) refers to the similarity of chemical elements, molecules or chemical compounds with respect to either structural or functional qualities, i.e. the consequence that the chemical chemical compound has on reaction partners in inorganic or biological settings. Biological furnishings and thus besides similarity of effects are usually quantified using the biological activity of a compound. In general terms, role can exist related to the chemical activeness of compounds (among others).

The notion of chemical similarity (or molecular similarity) is one of the near important concepts in cheminformatics.^{[1] [2]} It plays an of import role in mod approaches to predicting the properties of chemic compounds, designing chemicals with a predefined set up of properties and, especially, in conducting drug design studies by screening big databases containing structures of available (or potentially available) chemicals. These studies are based on the similar belongings principle of Johnson and Maggiora, which states: like compounds have similar properties.^[1]

Similarity measures [edit]

Chemical similarity is oft described equally an inverse of a measure of distance in descriptor infinite. Examples for inverse distance measures are molecule kernels, that measure out the structural similarity of chemical compounds.^[3]

Similarity search and virtual screening [edit]

The similarity-based^[4] virtual screening (a kind of ligand-based virtual screening) assumes that all compounds in a database that are similar to a query compound accept similar biological activity. Although this hypothesis is not always valid,^[v] quite frequently the set of retrieved compounds is considerably enriched with actives.^[vi] To achieve high efficacy of similarity-based screening of databases containing millions of compounds, molecular structures are unremarkably represented by molecular screens (structural keys) or by fixed-size or variable-size molecular fingerprints. Molecular screens and fingerprints can contain both 2D- and 3D-data. However, the 2D-fingerprints, which are a kind of binary fragment descriptors, dominate in this area. Fragment-based structural keys, like MDL keys,^[7] are sufficiently expert for treatment small and medium-sized chemic databases, whereas processing of big databases is performed with fingerprints having much higher data density. Fragment-based Daylight,^[8] BCI,^[9] and UNITY 2D (Tripos^[10]) fingerprints are the all-time known examples. The almost popular similarity measure out for comparison chemic structures represented by means of fingerprints is the Tanimoto (or Jaccard) coefficient T. 2 structures are usually considered similar if T > 0.85 (for Daylight fingerprints). However, it is a mutual misunderstanding that a similarity of T > 0.85 reflects similar bioactivities in full general ("the 0.85 myth").^[11]

Chemical similarity network [edit]

The concept of chemical similarity can be expanded to consider chemic similarity network theory, where descriptive network backdrop and graph theory tin be applied to analyze large chemical space, estimate chemic diversity and predict drug target. Recently, 3D chemical similarity networks based on 3D ligand conformation accept too been developed, which tin exist used to place scaffold hopping ligands.

Encounter also [edit]

• Me-too compound
• Drug pattern

References [edit]

1. ^ ^{a b} Johnson, A. M.; Maggiora, G. Yard. (1990). Concepts and Applications of Molecular Similarity. New York: John Willey & Sons. ISBN978-0-471-62175-ane.
2. ^ North. Nikolova; J. Jaworska (2003). "Approaches to Measure Chemical Similarity - a Review". QSAR & Combinatorial Science. 22 (nine–10): 1006–1026. doi:ten.1002/qsar.200330831.
3. ^ Ralaivola, Liva; Swamidass, Sanjay J.; Hiroto, Saigo; Baldi, Pierre (2005). "Graph kernels for chemical informatics". Neural Networks. 18 (8): 1093–1110. doi:10.1016/j.neunet.2005.07.009. PMID 16157471.
4. ^ Rahman, Southward. A.; Bashton, Thousand.; Holliday, K. Fifty.; Schrader, R.; Thornton, J. M. (2009). "Minor Molecule Subgraph Detector (SMSD) toolkit". Journal of Cheminformatics. ane (12): 12. doi:10.1186/1758-2946-ane-12. PMC2820491. PMID 20298518.
5. ^ Kubinyi, H. (1998). "Similarity and Dissimilarity: A Medicinal Chemist's View". Perspectives in Drug Discovery and Blueprint. nine–11: 225–252. doi:10.1023/A:1027221424359.
6. ^ Martin, Y. C.; Kofron, J. Fifty.; Traphagen, 50. M. (2002). "Do structurally similar molecules accept similar biological activity?". J. Med. Chem. 45 (19): 4350–4358. doi:10.1021/jm020155c. PMID 12213076.
7. ^ Durant, J. L.; Leland, B. A.; Henry, D. R.; Nourse, J. Chiliad. (2002). "Reoptimization of MDL Keys for Utilise in Drug Discovery". J. Chem. Inf. Comput. Sci. 42 (6): 1273–1280. doi:10.1021/ci010132r. PMID 12444722.
8. ^ "Daylight Chemic Data Systems Inc".
9. ^ "Barnard Chemical Information Ltd". Archived from the original on 2008-ten-11.
10. ^ "Tripos Inc".
11. ^ Maggiora, 1000.; Vogt, Thousand.; Stumpfe, D.; Bajorath, J. (2014). "Molecular Similarity in Medicinal Chemistry". J. Med. Chem. 57 (viii): 3186–3204. doi:10.1021/jm401411z. PMID 24151987.

External links [edit]

• Bender, Andreas; Glen, Robert C. (2004). "Molecular similarity: a key technique in molecular informatics". Organic & Biomolecular Chemistry. Royal Society of Chemical science (RSC). ii (22): 3204–xviii. doi:10.1039/b409813g. ISSN 1477-0520. PMID 15534697.
• Small-scale Molecule Subgraph Detector (SMSD)— a Java-based software library for calculating Maximum Common Subgraph (MCS) between small molecules. This enables usa to detect similarity/distance betwixt molecules. MCS is also used for screening drug like compounds by hitting molecules, which share common subgraph (substructure).
• Kernel-based Similarity for Clustering, regression and QSAR Modeling
• Brutus— a similarity analysis tool based on molecular interaction fields.

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Source: https://en.wikipedia.org/wiki/Chemical_similarity

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