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Discovery of Power-Laws in Chemical Space.

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Title: Discovery of Power-Laws in Chemical Space.
Authors: Ryan W. Benz, S. Joshua Swamidass, Pierre Baldi
Source: Journal of Chemical Information & Modeling. Jun2008, Vol. 48 Issue 6, p1138-1151. 14p.
Subjects: Chemical structure, Molecules, Resource allocation, Molecular structure, Exponents, Computer science, Chemistry, Computer software
Abstract: Power-law distributions have been observed in a wide variety of areas. To our knowledge however, there has been no systematic observation of power-law distributions in chemoinformatics. Here, we present several examples of power-law distributions arising from the features of small, organic molecules. The distributions of rigid segments and ring systems, the distributions of molecular paths and circular substructures, and the sizes of molecular similarity clusters all show linear trends on log−log rank/ frequencyplots, suggesting underlying power-law distributions. The number of unique features also follow Heaps’-like laws. The characteristic exponents of the power-laws lie in the 1.5−3 range, consistently with the exponents observed in other power-law phenomena. The power-law nature of these distributions leads to several applications including the prediction of the growth of available data through Heaps’ law and the optimal allocation of experimental or computational resources via the 80/20 rule. More importantly, we also show how the power-laws can be leveraged to efficiently compress chemical fingerprints in a lossless manner, useful for the improved storage and retrieval of molecules in large chemical databases. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Chemical Information & Modeling is the property of American Chemical Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Discovery of Power-Laws in Chemical Space.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Chemical+Information+%26+Modeling%22">Journal of Chemical Information & Modeling</searchLink>. Jun2008, Vol. 48 Issue 6, p1138-1151. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Chemical+structure%22">Chemical structure</searchLink><br /><searchLink fieldCode="DE" term="%22Molecules%22">Molecules</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+structure%22">Molecular structure</searchLink><br /><searchLink fieldCode="DE" term="%22Exponents%22">Exponents</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22Chemistry%22">Chemistry</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink>
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  Data: Power-law distributions have been observed in a wide variety of areas. To our knowledge however, there has been no systematic observation of power-law distributions in chemoinformatics. Here, we present several examples of power-law distributions arising from the features of small, organic molecules. The distributions of rigid segments and ring systems, the distributions of molecular paths and circular substructures, and the sizes of molecular similarity clusters all show linear trends on log−log rank/ frequencyplots, suggesting underlying power-law distributions. The number of unique features also follow Heaps’-like laws. The characteristic exponents of the power-laws lie in the 1.5−3 range, consistently with the exponents observed in other power-law phenomena. The power-law nature of these distributions leads to several applications including the prediction of the growth of available data through Heaps’ law and the optimal allocation of experimental or computational resources via the 80/20 rule. More importantly, we also show how the power-laws can be leveraged to efficiently compress chemical fingerprints in a lossless manner, useful for the improved storage and retrieval of molecules in large chemical databases. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of Journal of Chemical Information & Modeling is the property of American Chemical Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1021/ci700353m
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 14
        StartPage: 1138
    Subjects:
      – SubjectFull: Chemical structure
        Type: general
      – SubjectFull: Molecules
        Type: general
      – SubjectFull: Resource allocation
        Type: general
      – SubjectFull: Molecular structure
        Type: general
      – SubjectFull: Exponents
        Type: general
      – SubjectFull: Computer science
        Type: general
      – SubjectFull: Chemistry
        Type: general
      – SubjectFull: Computer software
        Type: general
    Titles:
      – TitleFull: Discovery of Power-Laws in Chemical Space.
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            NameFull: Ryan W. Benz
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            NameFull: S. Joshua Swamidass
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            NameFull: Pierre Baldi
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            – D: 05
              M: 06
              Text: Jun2008
              Type: published
              Y: 2008
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              Value: 48
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            – TitleFull: Journal of Chemical Information & Modeling
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