A bibliometric analysis of chemoinformatics

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A bibliometric analysis of chemoinformatics

• Presented at the 25th Anniversary Meeting of the
  Molecular Graphics and Modelling Society, School
  of Oriental and African Studies, London 13th
  March 2007
• Peter Willett, University of Sheffield, UK

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Overview of talk

• Bibliometrics
• Chemoinformatics
• Growth of the subject
• Subject coverage
• Author productivity
• The Journal of Molecular Graphics (and Modelling)

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Bibliometrics what is it?

• Bibliometrics is
• The application of mathematical and statistical
  methods to books and other media (A. Pritchard
  (1969), Statistical bibliography or
  bibliometrics?, J. Docum., Vol. 25, pp. 348-349)
• The study, or measurement, of texts and
  information (Wikipedia)
• See also
• Webometrics
• the study of the quantitative aspects of the
  construction and use of information resources,
  structures and technologies on the Web drawing on
  bibliometric and informetric approaches" (L.
  Björneborn and P. Ingwersen (2004), Toward a
  basic framework for webometrics. J. Amer. Soc.
  Inf. Sci. Technol., Vol. 55, pp. 1216-1227)
• Cybermetrics, informetrics, scientometrics

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Bibliometrics subjects of study

• Bibliometric distributions
• Highly skewed frequency distributions (Bradford,
  Lotka, Zipf) and their implications
• Citation analysis
• Analysis of individuals, institutions and
  journals
• Use as performance indicators for the evaluation
  of research
• Philosophy of science
• Subject coverage
• Academic collaborations
• Now extension to linkages between Web sites
• Sitations, cf citations

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From chemical documentation to chemoinformatics

• Chemical documentation is long established
• Chemisches Journal started in 1778
• Chemical Abstracts started in 1907
• First computer-based information systems and
  services in Sixties
• Chemical Titles in 1961
• Morgan and Sussenguth algorithms in 1965
• Recent emergence of chemoinformatics
• M. Hann and R. Green (1999), Chemoinformatics - a
  new name for an old problem?, Curr. Opin. Chem.
  Biol., Vol. 3, pp. 379-383.

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Chemoinformatics definitions

• The use of information technology and management
  has become a critical part of the drug discovery
  process. Chemoinformatics is the mixing of those
  information resources to transform data into
  information and information into knowledge for
  the intended purpose of making better decisions
  faster in the area of drug lead identification
  and optimization F.K. Brown (1998),
  Chemoinformatics What is it and how does it
  impact drug discovery?, Ann. Reports Med. Chem.,
  Vol. 33, pp. 375-384
• Take 1998 as the starting point for the
  bibliometric analyses
• Many alternatives, e.g.
• Chem(o)informatics is a generic term that
  encompasses the design, creation, organization,
  management, retrieval, analysis, dissemination,
  visualization and use of chemical information
  G Paris (August 1999 ACS meeting), quoted by
  W.A. Warr at http//www.warr.com/warrzone.htm
• Chemoinformatics is the application of
  informatics methods to solve chemical problems
  J. Gasteiger and T. Engels (2003),
  Chemoinformatics a Textbook, Wiley-VCH.

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Bibliometric studies in chemoinformatics

• Onodera (2001)
• Analysis of the subject coverage of Journal of
  Chemical Information and Computer Sciences
• Redman et al. (2001)
• Applications of the Cambridge Structural Database
  
• Bishop et al. (2003)
• Citations to Sheffield chemoinformatics research
• Warr (2005)
• Most cited papers in Journal of Chemical
  Information and Computer Sciences
• Behrens and Luksch (2006)
• Contents of the Inorganic Crystal Structure
  Database

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Data sources for bibliometric research

• Web of Knowledge (WOK)
• Long established as the data source for
  bibliometric analyses
• Recent addition of analysis tools (Analyse
  Results and Citation Reports)
• Probably still the most comprehensive
• New sources
• Google
• Google Scholar restricted to the scholarly
  literature
• Scopus
• New service from Elsevier, offering similar
  facilities to WOK

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What shall we call it?
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Google postings from http//www.molinspiration.com
/chemoinformatics.html
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• WOK search of the title, keyword and abstract
  fields for
• chemoinformatics OR cheminformatics OR chemical
  informatics
• This search retrieved 197 records for the period
  1998-2006 in 87 different sources
• Of these, Journal of Chemical Information and
  Modeling (and its predecessor) is clearly the
  core journal

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Most frequently occurring sources
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Inter-journal relationships

• L. Leydesdorff (2007), "Visualization of the
  citation impact environment of scientific
  journals", J. Amer. Soc. Inf. Sci. Technol., Vol.
  58, pp. 25-38.
• Analysis of 2003-04 WOK data to identify journals
  that provide gt 1 of the citations to/from a
  given journal
• For Journal of Chemical Information and Computer
  Sciences
• 14 other to journals but only 5 other from
  journals
• Multi-disciplinary nature of the field means that
  a wide range of sources are used

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Author productivity I

• Analysis of the authors of all articles published
  1998-2006 in
• Bioinformatics, Combinatorial Chemistry and
  High-Throughput Screening and Journal of
  Biomolecular Screening
• Journal of Chemical Information and Modeling,
  Journal of Computer-Aided Molecular Design,
  Molecular Diversity and QSAR Combinatorial
  Science
• Journal of Molecular Graphics and Modelling,
  Journal of Molecular Modeling and SAR and QSAR in
  Environmental Research
• Identification of the 20 most productive authors
  for each of these journals in 1998-2006

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Author productivity II

• Productive authors in the first group of journals
  did not publish frequently in the other two
  groups of journals, but fair degree of overlap
  between the journals in the other two groups
  (Molecular Diversity the least)
• There is one author in the top-20 for four
  journals, two authors in the top-20 for three
  journals and 12 authors in the top-20 for two
  journals
• Eight of the top-20 authors in Journal of
  Chemical Information and Computer Sciences are
  also top-20 authors in other journals
• Main degrees of overlap between
• Journal of Chemical Information and Modeling and
  Journal of Computer-Aided Molecular Design
• QSAR Combinatorial Science and SAR and QSAR in
  Environmental Research

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Overlap in top-20 authors
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The core literature

• A basic principle of bibliometrics is that
  citation corresponds to use, i.e., frequently
  cited papers are the most scientifically valuable
• NB the many exceptions
• Classic citations
• Critical citations
• Self-citation and close collaborators
• Journal Impact Factor games
• but generally a valid assumption
• Analysis of citations to 4411 articles in seven
  chemoinformatics journals for 1998-2006 attracted
  a total of 35,228 citations

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Most-cited papers I
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Most-cited papers II

• Certain types of article strongly represented in
  the top-30 positions
• Software descriptions (9)
• Reviews (4)
• Drug-likeness (4)
• Binding energies (4)
• The first of these might be thought of as the
  fields classic citations (cf Journal of
  Chemical Information and Computer Sciences two
  most-cited articles)

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Institutional productivity

• The following institutions all provide at least
  1 of the papers in all of the seven journals
• National Institute of Chemistry, Ljubljana,
  University of Erlangen-Nurnberg, University of
  Sheffield, University of Minnesota, Environmental
  Protection Agency, Russian Academy of Sciences,
  Liverpool John Moores University, Pennsylvania
  State University, Chinese Academy of Sciences and
  the University of Cambridge
• Of top-50 institutions, only Tripos (no. 27) and
  Pfizer (no. 36) are for-profit organisations

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National productivity the ten countries
providing the most articles in the seven journals
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The Journal of Molecular Graphics and Modelling

• The journal, then the Journal of Molecular
  Graphics, was started in 1983 and changed to its
  current name with Volume 15 in 1997
• The journal is
• devoted to the publication of papers on the uses
  of computers in theoretical investigations of
  molecular structure, function, interaction, and
  design. The scope of the journal includes all
  aspects of molecular modelling and computational
  chemistry, including, for instance, the study of
  molecular shape and properties, molecular
  simulations, protein and polymer engineering,
  drug design, materials design, structure-activity
  and structure-property relationships, database
  mining, and compound library design
• See http//www.elsevier.com/wps/find/journaldescri
  ption.cws_home/525012/descriptiondescription

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Bibliometric distributions I

• Many bibliometric distributions are characterised
  by inverse, highly skewed frequency distributions
• Zipfs Law for word occurrences
• Lotkas Law for author productivity
• Bradfords Law for subject spread in journals
• Many other examples
• Design of storage systems
• Language acquisition
• Income distribution (Pareto distribution)

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Bibliometric distributions II

• All of the bibliometric distributions can be
  represented by an equation of the form
• where f(k) is the frequency of occurrence of
  some bibliometric item that is associated with
  each member of a population (k1,2...) that is
  producing examples of these items, and where C
  and ? are constants

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Lotkas Law

• The original formulation (A. Lotka (1926), The
  frequency distribution of scientific
  productivity, Journal of the Washington Academy
  of Sciences, Vol. 16, pp. 317-323) suggested ?2
  but wide range of values observed in practice,
  e.g., 1.78-3.78 (M.L. Pao (1986), An empirical
  examination of Lotka's Law, J. Amer. Soc. Inf.
  Sci., Vol. 37, pp. 26-33)
• WOK lists 859 articles appearing in Vols. 2-24 of
  the journal
• Reasonable Lotka plot with C0.834 and ? 3.02
• Well know authors with gt 6 papers Arteca,
  Bajorath, Brasseur, Chatterjee, Ferrin, Flower,
  Gaber, Goodsell, Griffith, Maigret, Martin,
  Mornon, Nakamura, Olson, Richards, Tapia, Toma,
  Umeyama, Welsh, White, Willett

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Lotka data for 859 articles published in Volumes
2-24 of the journal
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Types of paper in Volumes 4 (1986), 14 (1996) and
24 (2006)
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Most-cited papers
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Inter-journal relatedness

• The Journal Citation Reports database provides a
  further way of analysing the degree of
  co-citation between journals
• Let A and B be journals publishing PA and PB
  articles let CAB be the number of times that A
  cites B and let CTA be the total number of
  citations in A. Then the relatedness of A to B
  is defined as
• A similar calculation can be made of the
  relatedness of B to A

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Relatedness values ( 106)
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Countries providing at least 3 of the articles
in Volumes 2-24 of the journal
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Conclusions

• Most academics are interested in their personal
  citation counts and in the impact factors for
  their favourite journals
• Bibliometrics has more general applications
• Subject coverage
• Key players and articles
• Relationships between journals
• Recent developments facilitate the carrying-out
  of such analyses