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Chemical database

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A chemical database is a database specifically designed to store chemical information. This information is about chemical and crystal structures, spectra, reactions and syntheses, and thermophysical data.

Types of chemical databases

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Bioactivity database

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Bioactivity databases correlate structures or other chemical information to bioactivity results taken from bioassays in literature, patents, and screening programs.

Name Developer(s) Initial release
ScrubChem Jason Bret Harris 2016[1][2]
PubChem-BioAssay NIH  2004[3][4]
ChEMBL EMBL-EBI 2009[5]

Chemical structures

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Chemical structures are traditionally represented using lines indicating chemical bonds between atoms and drawn on paper (2D structural formulae). While these are ideal visual representations for the chemist, they are unsuitable for computational use and especially for search and storage. Small molecules (also called ligands in drug design applications), are usually represented using lists of atoms and their connections. Large molecules such as proteins are however more compactly represented using the sequences of their amino acid building blocks. Radioactive isotopes are also represented, which is an important attribute for some applications. Large chemical databases for structures are expected to handle the storage and searching of information on millions of molecules taking terabytes of physical memory.[6][7]

Literature database

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Chemical literature databases correlate structures or other chemical information to relevant references such as academic papers or patents. This type of database includes STN, Scifinder, and Reaxys. Links to literature are also included in many databases that focus on chemical characterization.

Crystallographic database

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Crystallographic databases store X-ray crystal structure data. Common examples include Protein Data Bank and Cambridge Structural Database.

NMR spectra database

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NMR spectra databases correlate chemical structure with NMR data. These databases often include other characterization data such as FTIR and mass spectrometry.

Reactions database

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Most chemical databases store information on stable molecules but in databases for reactions also intermediates and temporarily created unstable molecules are stored. Reaction databases contain information about products, educts, and reaction mechanisms.

A popular example that lists chemical reaction data, among others, would be the Beilstein database.

Thermophysical database

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Thermophysical data are information about

Chemical structure representation

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There are two principal techniques for representing chemical structures in digital databases

These approaches have been refined to allow representation of stereochemical differences and charges as well as special kinds of bonding such as those seen in organo-metallic compounds. The principal advantage of a computer representation is the possibility for increased storage and fast, flexible search.

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Substructure

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Main article: Substructure search
The drug lenalidomide contains substructures isoindoline (red) and glutarimide (blue)

Chemists can search databases using parts of structures, parts of their IUPAC names as well as based on constraints on properties. Chemical databases are different from other general purpose databases in their support for substructure search, a method to retrieve chemicals matching a pattern of atoms and bonds which a user specifies. This kind of search is achieved by looking for subgraph isomorphism (sometimes also called a monomorphism) and is a widely studied application of graph theory.[8][9][10]

Query structures may contain bonding patterns such as "single/aromatic" or "any" to provide flexibility. Similarly, the vertices which in an actual compound would be a specific atom may be replaced with an atom list in the query. Cis–trans isomerism at double bonds is catered for by giving a choice of retrieving only the E form, the Z form, or both.[8][11]

Conformation

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Search by matching 3D conformation of molecules or by specifying spatial constraints is another feature that is particularly of use in drug design. Searches of this kind can be computationally very expensive. Many approximate methods have been proposed, for instance BCUTS,[12][13][14] special function representations, moments of inertia, ray-tracing histograms, maximum distance histograms, shape multipoles to name a few.[15][16][17][18][19]

Examples

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Large databases, such as PubChem[11][20] and ChemSpider,[21] have graphical interfaces for search. The Chemical Abstracts Service provides tools to search the chemical literature and Reaxys supplied by Elsevier covers both chemicals and reaction information, including that originally held in the Beilstein database.[22] PATENTSCOPE makes chemical patents accessible by substructure[23] and Wikipedia's articles describing individual chemicals can also be searched that way.[24]

Suppliers of chemicals as synthesis intermediates or for high-throughput screening routinely provide search interfaces. Currently, the largest database that can be freely searched by the public is the ZINC database, which is claimed to contain over 37 billion commercially available molecules.[25][26]

Descriptors

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All properties of molecules beyond their structure can be split up into either physico-chemical or pharmacological attributes also called descriptors. On top of that, there exist various artificial and more or less standardized naming systems for molecules that supply more or less ambiguous names and synonyms. The IUPAC name is usually a good choice for representing a molecule's structure in a both human-readable and unique string although it becomes unwieldy for larger molecules. Trivial names on the other hand abound with homonyms and synonyms and are therefore a bad choice as a defining database key. While physico-chemical descriptors like molecular weight, (partial) charge, solubility, etc. can mostly be computed directly based on the molecule's structure, pharmacological descriptors can be derived only indirectly using involved multivariate statistics or experimental (screening, bioassay) results. All of those descriptors can for reasons of computational effort be stored along with the molecule's representation and usually are.

Similarity

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Main article: Chemical similarity

There is no single definition of molecular similarity, however the concept may be defined according to the application and is often described as an inverse of a measure of distance in descriptor space. Two molecules might be considered more similar for instance if their difference in molecular weights is lower than when compared with others. A variety of other measures could be combined to produce a multi-variate distance measure. Distance measures are often classified into Euclidean measures and non-Euclidean measures depending on whether the triangle inequality holds. Maximum Common Subgraph (MCS) based substructure search [27](similarity or distance measure) is also very common. MCS is also used for screening drug like compounds by hitting molecules, which share common subgraph (substructure).[28]

Chemicals in the databases may be clustered into groups of 'similar' molecules based on similarities. Both hierarchical and non-hierarchical clustering approaches can be applied to chemical entities with multiple attributes. These attributes or molecular properties may either be determined empirically or computationally derived descriptors. One of the most popular clustering approaches is the Jarvis-Patrick algorithm.[29]

In pharmacologically oriented chemical repositories, similarity is usually defined in terms of the biological effects of compounds (ADME/tox) that can in turn be semiautomatically inferred from similar combinations of physico-chemical descriptors using QSAR methods.

Registration systems

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Databases systems for maintaining unique records on chemical compounds are termed as Registration systems. These are often used for chemical indexing, patent systems and industrial databases.

Registration systems usually enforce uniqueness of the chemical represented in the database through the use of unique representations. By applying rules of precedence for the generation of stringified notations, one can obtain unique/'canonical' string representations such as 'canonical SMILES'. Some registration systems such as the CAS system make use of algorithms to generate unique hash codes to achieve the same objective.

A key difference between a registration system and a simple chemical database is the ability to accurately represent that which is known, unknown, and partially known. For example, a chemical database might store a molecule with stereochemistry unspecified, whereas a chemical registry system requires the registrar to specify whether the stereo configuration is unknown, a specific (known) mixture, or racemic. Each of these would be considered a different record in a chemical registry system.

Registration systems also preprocess molecules to avoid considering trivial differences such as differences in halogen ions in chemicals.

An example is the Chemical Abstracts Service (CAS) registration system. See also CAS registry number.

List of Chemical Cartridges

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List of Chemical Registration Systems

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Web-based

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Name Developer(s) Initial release
CDD Vault Collaborative Drug Discovery  2018[40][41][42]
Adroit Repository[43] Adroit DI[44] 2023[45][46]

Tools

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The computational representations are usually made transparent to chemists by graphical display of the data. Data entry is also simplified through the use of chemical structure editors. These editors internally convert the graphical data into computational representations.

There are also numerous algorithms for the interconversion of various formats of representation. An open-source utility for conversion is OpenBabel. These search and conversion algorithms are implemented either within the database system itself or as is now the trend is implemented as external components that fit into standard relational database systems. Both Oracle and PostgreSQL based systems make use of cartridge technology that allows user defined datatypes. These allow the user to make SQL queries with chemical search conditions (For example, a query to search for records having a phenyl ring in their structure represented as a SMILES string in a SMILESCOL column could be 

 SELECT * FROM CHEMTABLE WHERE SMILESCOL.CONTAINS('c1ccccc1')

Algorithms for the conversion of IUPAC names to structure representations and vice versa are also used for extracting structural information from text. However, there are difficulties due to the existence of multiple dialects of IUPAC. Work is on to establish a unique IUPAC standard (See InChI).

See also

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References

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  1. ^ "Home Page - ScrubChem". scrubchem.org. Archived from the original on 26 May 2017.
  2. ^ Harris, JB (2019). "Post-processing of Large Bioactivity Data". Bioinformatics and Drug Discovery. Methods Mol Biol. Vol. 1939. pp. 37–47. doi:10.1007/978-1-4939-9089-4_3. ISBN 978-1-4939-9088-7. PMID 30848455. S2CID 73493315.
  3. ^ "PubChem". pubchem.ncbi.nlm.nih.gov.
  4. ^ Wang, Y; Bryant, SH; Cheng, T; Wang, J; Gindulyte, A; Shoemaker, BA; Thiessen, PA; He, S; Zhang, J (2017). "PubChem BioAssay: 2017 update". Nucleic Acids Res. 45 (D1): D955–D963. doi:10.1093/nar/gkw1118. PMC 5210581. PMID 27899599.
  5. ^ "ChEMBL Database".
  6. ^ Hoffmann, Torsten; Gastreich, Marcus (2019). "The next level in chemical space navigation: going far beyond enumerable compound libraries". Drug Discovery Today. 24 (5): 1148–1156. doi:10.1016/j.drudis.2019.02.013. PMID 30851414.
  7. ^ Sadybekov, Anastasiia V.; Katritch, Vsevolod (2023). "Computational approaches streamlining drug discovery". Nature. 616 (7958): 673–685. Bibcode:2023Natur.616..673S. doi:10.1038/s41586-023-05905-z. PMID 37100941.
  8. ^ a b Currano, Judith N. (2014). "Chapter 5. Searching by Structure and Substructure". Chemical Information for Chemists. pp. 109–145. doi:10.1039/9781782620655-00109. ISBN 978-1-84973-551-3.
  9. ^ Ullmann, J. R. (1976). "An Algorithm for Subgraph Isomorphism". Journal of the ACM. 23: 31–42. doi:10.1145/321921.321925.
  10. ^ Warr, Wendy A. (2011). "Representation of chemical structures". WIREs Computational Molecular Science. 1 (4): 557–579. doi:10.1002/wcms.36.
  11. ^ a b "PubChem Structure Search". pubchem.ncbi.nlm.nih.gov. Retrieved 2024-08-01.
  12. ^ Pearlman, R.S.; Smith, K.M. (1999). "Metric Validation and the Receptor-Relevant Subspace Concept". J. Chem. Inf. Comput. Sci. 39: 28–35. doi:10.1021/ci980137x.
  13. ^ "BCUTDescriptor (cdk 2.5 API)". CDK - Chemistry Development Kit. 2021-05-05. Retrieved 2024-06-04.
  14. ^ Burden, Frank R. (1 August 1989). "Molecular identification number for substructure searches". Journal of Chemical Information and Computer Sciences. 29 (3): 225–227. doi:10.1021/ci00063a011.
  15. ^ Pearlman, R.S.; Smith, K.M. (1999). "Metric Validation and the Receptor-Relevant Subspace Concept". J. Chem. Inf. Comput. Sci. 39: 28–35. doi:10.1021/ci980137x.
  16. ^ Lin, Jr., Hung; Clark, Timothy (2005). "An analytical, variable resolution, complete description of static molecules and their intermolecular binding properties". Journal of Chemical Information and Modeling. 45 (4): 1010–1016. doi:10.1021/ci050059v. PMID 16045295.
  17. ^ Meek, P. J.; Liu, Z.; Tian, L.; Wang, C. J; Welsh, W. J; Zauhar, R. J (2006). "Shape Signatures: speeding up computer aided drug discovery". DDT 2006. 19–20 (19–20): 895–904. doi:10.1016/j.drudis.2006.08.014. PMID 16997139.
  18. ^ Grant, J. A; Gallardo, M. A.; Pickup, B. T. (1996). "A fast method of molecular shape comparison: A simple application of a Gaussian description of molecular shape". Journal of Computational Chemistry. 17 (14): 1653–1666. doi:10.1002/(sici)1096-987x(19961115)17:14<1653::aid-jcc7>3.0.co;2-k. S2CID 96794688.
  19. ^ Ballester, P. J.; Richards, W. G. (2007). "Ultrafast shape recognition for similarity search in molecular databases". Proceedings of the Royal Society A. 463 (2081): 1307–1321. Bibcode:2007RSPSA.463.1307B. doi:10.1098/rspa.2007.1823. S2CID 12540483.
  20. ^ Kim, Sunghwan (2021). "Exploring Chemical Information in PubChem". Current Protocols. 1 (8): e217. doi:10.1002/cpz1.217. PMC 8363119. PMID 34370395.
  21. ^ Williams, Antony J. (2010). "ChemSpider: Integrating Structure-Based Resources Distributed across the Internet". Enhancing Learning with Online Resources, Social Networking, and Digital Libraries. ACS Symposium Series. Vol. 1060. pp. 23–39. doi:10.1021/bk-2010-1060.ch002. ISBN 978-0-8412-2600-5.
  22. ^ Jarabak, Charlotte; Mutton, Troy; Ridley, Damon D. (2020). "Property Information in Substance Records in Major Web-Based Chemical Information and Data Retrieval Tools: Understanding Content, Search Opportunities, and Application to Teaching". Journal of Chemical Education. 97 (5): 1345–1359. Bibcode:2020JChEd..97.1345J. doi:10.1021/acs.jchemed.9b00966.
  23. ^ "Substructure Search Now Available in PATENTSCOPE". www.wipo.int. 2019-02-11. Retrieved 2024-08-04.
  24. ^ Ertl, Peter; Patiny, Luc; Sander, Thomas; et al. (2015). "Wikipedia Chemical Structure Explorer: Substructure and similarity searching of molecules from Wikipedia". Journal of Cheminformatics. 7: 10. doi:10.1186/s13321-015-0061-y. PMC 4374119. PMID 25815062.
  25. ^ Tingle, Benjamin I.; Tang, Khanh G.; Castanon, Mar; Gutierrez, John J.; Khurelbaatar, Munkhzul; Dandarchuluun, Chinzorig; Moroz, Yurii S.; Irwin, John J. (2023). "ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery". Journal of Chemical Information and Modeling. 63 (4): 1166–1176. doi:10.1021/acs.jcim.2c01253. PMC 9976280. PMID 36790087.
  26. ^ Warr, Wendy A.; Nicklaus, Marc C.; Nicolaou, Christos A.; Rarey, Matthias (2022). "Exploration of Ultralarge Compound Collections for Drug Discovery". Journal of Chemical Information and Modeling. 62 (9): 2021–2034. doi:10.1021/acs.jcim.2c00224. PMID 35421301.
  27. ^ Rahman, S. A.; Bashton, M.; Holliday, G. L.; Schrader, R.; Thornton, J. M. (2000). "Small Molecule Subgraph Detector (SMSD) toolkit". Journal of Cheminformatics. 1 (1): 12. doi:10.1186/1758-2946-1-12. PMC 2820491. PMID 20298518.
  28. ^ Rahman, S. Asad; Bashton, M.; Holliday, G. L.; Schrader, R.; Thornton, J. M. (2009). "Small Molecule Subgraph Detector (SMSD) Toolkit". Journal of Cheminformatics. 1 (1): 12. doi:10.1186/1758-2946-1-12. PMC 2820491. PMID 20298518.
  29. ^ Butina, Darko (1999). "Unsupervised Data Base Clustering Based on Daylight's Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets". Chem. Inf. Comput. Sci. 39 (4): 747–750. doi:10.1021/ci9803381.
  30. ^ "BIOVIA Direct - BIOVIA - Dassault Systèmes®". 8 September 2023.
  31. ^ "JChem Engines | ChemAxon".
  32. ^ "Chemistry – Oracle Cartridge | Inside Informatics".
  33. ^ Pavlov, D.; Rybalkin, M.; Karulin, B. (2010). "Bingo from SciTouch LLC: Chemistry cartridge for Oracle database". Journal of Cheminformatics. 2 (Suppl 1): F1. doi:10.1186/1758-2946-2-S1-F1. PMC 2867114.
  34. ^ "Small Molecule Drug Discovery Software". Small Molecule Drug Discovery Software.
  35. ^ "BIOVIA Chemical Registration - BIOVIA - Dassault Systèmes®". www.3ds.com. 7 September 2023.
  36. ^ "Register". Archived from the original on 2021-12-10. Retrieved 2021-03-13.
  37. ^ "Scilligence RegMol | Scilligence". 6 June 2016.[permanent dead link]
  38. ^ "Compound Registration". chemaxon.com.
  39. ^ "Signals Notebook - PerkinElmer Informatics". perkinelmerinformatics.com.
  40. ^ "CDD Vault Update: CDD Vault is Now an ELN". 16 February 2018.
  41. ^ "CDD Electronic Lab Notebook (ELN)". 14 August 2019.
  42. ^ "Electronic Lab Notebooks: What they are (And why you need one)". 4 August 2019.
  43. ^ "Review of SDF Pro from Adroit DI. June 2023 – Macs in Chemistry". 2023-11-05. Retrieved 2024-03-11.
  44. ^ "Adroit DI main page". adroitdi.com. Retrieved 2024-03-10.
  45. ^ "Adroit DI's SDF Pro: The Fast and Affordable Solution to Storing, Sorting and Wrangling 10 Million Molecules in Seconds". www.businesswire.com. 2023-05-16. Retrieved 2024-03-10.
  46. ^ "Best of the Best Entity Registration". 20Visioneers15. Retrieved 2024-03-10.
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