A Bibliometric Study of Quantitative Structure-ActivityRelationships and QSAR & Combinatorial Science

Peter Willett*

Department of Information Studies, University of Sheffield, 211 Portobello Street, Sheffield S14DP, UK*E-mail: p.willett@sheffield.ac.uk; Telephone: 0044-114-2222633

Keywords: Authors, Bibliometrics, Citations, Publications, Web of Knowledge database

Received: August 20, 2009; Accepted: September 24, 2009

DOI: 10.1002/qsar.200988888

AbstractThis paper reviews the articles published in Volumes 4 – 27 of the journals QuantitativeStructure-Activity Relationships and of QSAR & Combinatorial Science, focusing on thearticles published in the journals, citations to those articles, the most productive authorsand countries, and the relationship of the journals to the more general chemicalliterature.

1 Introduction

The journal Quantitative Structure-Activity Relationshipswas established in 1982 and set out to cover theoretical,methodological and innovative applications of QSAR. Itchanged its name to QSAR & Combinatorial Science in2003, with the aim of broadening the focus of the journal[1]. The website states that “QSAR & Combinatorial Sci-ence is the international forum for high-quality results onall aspects of computer-assisted methods and combinatori-al techniques including cheminformatics, bioinformatics,chemometrics, virtual screening, and molecular modeling”.However, an inspection of the journal�s contents showsthat it continues to focus more strongly on the QSAR andmodeling aspects of its remit, and there are now severalother journals with a more specific focus on combinatorialscience, such as Combinatorial Chemistry & High-Throughput Screening, the Journal of CombinatorialChemistry and Molecular Diversity. It has hence been de-cided that the journal should further broaden its focus,with the new journal, Molecular Informatics, providingcoverage from 2010 on all molecular aspects of bioinfor-matics, chemoinformatics and computer-assisted molecu-lar design [2]. This latest change provides an opportunityto review the history of the journal to date, specifically toreview the history as reflected in a bibliometric analysis ofthe papers that have appeared in the journal since its in-ception. More general reviews of the historical develop-ment of QSAR are provided by Martin [3], Kubinyi [4]and Selassie [5].

Bibliometrics is “The application of mathematical andstatistical methods to books and other media” [6] and in-volves the numeric analysis of bibliographic data such asthe authors of papers, places of publication, citations to a

specific paper or papers. Bibliometrics is now a well-estab-lished discipline with at least two journals devoted specifi-cally to it – the Journal of Informetrics and Scientometrics -and with bibliometric articles appearing in an increasinglywide range of journals. Many of these articles involve theuse of citation data to discuss the historical developmentof a discipline, the identification of the key researchersand key collaborations, and the impact of the research car-ried out by an individual scientist or research group interalia [7 – 9]. Chemoinformatics is one discipline where bib-liometric papers are starting to appear, although the litera-ture is still quite limited [10 – 15]. Here, we report a biblio-metric analysis of Quantitative Structure-Activity Relation-ships and QSAR & Combinatorial Science, focusing on thearticles that have appeared in the journal and the citationsto those publications. In this paper, we shall normally useQSAR to refer to the journal in general, with the individu-al names only being used where it is necessary to differen-tiate between Quantitative Structure-Activity Relationshipsand QSAR & Combinatorial Science.

2 Methods

There are three broad-based sources of bibliometric data:the Web of Knowledge (WOK) produced by ThomsonReuters; Scopus produced by Elsevier B.V.; and GoogleScholar, produced by Google Inc. The work reported herehas used WOK for two principal reasons. First, GoogleScholar, does not, as yet at least, have the sophisticated an-alytic and postprocessing facilities that enable bibliometricanalyses to be carried out on WOK and Scopus extremelyquickly. Second, inspection of a few key papers (chosenfrom Table 2 below) showed that they had higher citation

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counts in WOK than in Scopus. It should be noted there isa short gap in WOK�s coverage around the time that thejournal changed its name to QSAR & Combinatorial Sci-ence. Data is available for Volume 4 part 1 through to Vol-ume 21 part 6 (which was published in December 2002,when the journal appeared for the last time under its origi-nal title) but then does not recommence till Volume 22part 4 (which was published in June 2003). With this ex-ception, our analyses covered all of the papers appearingin Volumes 4 – 27, covering the period 1985 to 2008 inclu-sive.

WOK is based on a detailed analysis of the papers in ca.10000 of the world�s top academic journals in the sciences,social sciences and arts and humanities. The database pro-vides access to the bibliographic data associated with all ofthe papers in these core journals, and to the articles thatare cited by these papers. The coverage of the databasehas recently been augmented by the inclusion of publica-tions and citations from high-profile conferences. Some ofthe analysis made use of the Journal Citation Reports(JCR) part of WOK: this contains extensive bibliometricinformation relating to individual journals (rather than toindividual publications and citation). All of the databasesearches were carried out in July 2009.

3 Results

3.1 Publications and Citations

A total of 1045 publications was identified as having ap-peared in the journal in Volumes 4 – 27. The results pre-sented below are based on analysis of the 870 articles, withthe remaining 175 publications including reviews, editorialmatters, corrections, etc. Of the articles, 470 appeared inthe 18 volumes of Quantitative Structure Activity Relation-ships and 400 in the six volumes of QSAR and Combinato-rial Science; that both journals have carried approximatelythe same numbers of articles is due to the greater numberof issues each year of the current journal.

The journal�s 870 articles were written by a total of 2086distinct authors. Inspection of the articles shows ten indi-viduals (many of them very well known in the field) whohave been associated with ten or more publications todate. These authors are: Fan, Roy and Schafer (all with 14articles); Cronin, Seydel and Weise (all with 12 articles)and Fujita, Kier, Mekenyan, Raevsky and Wold (all with11 articles). No less than 1623 individuals (77.8% of theauthors) were associated with just a single article. Thishighly skewed distribution, in which a few authors arehighly productive and most authors provide a single con-tribution, is known as Lotka�s Law and is apparent in bib-liometric analyses across a wide range of disciplines [16].

If we now consider the 62 countries from which the au-thors� articles come, rather than the authors of those arti-cles themselves, then it will come as little surprise to findthat the USA is the most productive nation. This is shownin Table 1, which summarizes the 862 articles for whichcountry data was available. The table is in two parts, onefor each of the journal titles (i.e., covering the periods1985 – 2002 and 2003 – 2008). The increasing productivityof the People�s Republic of China is very clear: it was the17th equal (with the Federal Republic of German) mostproductive country for Quantitative Structure-Activity Re-lationships but the second most productive country forQSAR & Combinatorial Science. The growth in impor-tance that is evident in Table 1 reflects the development ofthe country�s research more generally [17, 18]. The twoother notable additions in QSAR & Combinatorial Scienceare India and Iran, which were ranked 15th equal and 30th

equal, respectively, in Quantitative Structure-Activity Rela-tionships.

The 870 articles attracted a total of 10923 citations, i.e.,a mean of 12.55 citations per article, with the 20 most-citedarticles listed in Table 2. Many of these will be very famili-ar to readers of the journal, covering a range of topics thathave long been central to QSAR in general and to QSARin particular. Kier has three articles included in this mostcited list, whilst Cramer, Kubinyi and Willett all have twopapers included. Citation counts build up over time and itis hence hardly surprising that all of the articles in Table2

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Table 1. The ten countries publishing most articles in QSAR.

Quantitative Structure-Activity Relationships QSAR & Combinatorial Science

Country Publications Country Publications

USA 112 USA 75Germany 69 People�s Republic of China 61Italy 52 Germany 50UK 49 India 39Japan 33 UK 38Sweden 26 France 33Netherlands 23 Spain 25Switzerland 22 Iran 23France 21 Netherlands 16Spain 21 Japan 14

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are from Quantitative Structure-Activity Relationships,with none of them published later than 1997. Indeed, theonly article in the top-40 positions that has appeared sincethe journal changed its title is a paper with 52 citationsthat was ranked 27th (P Schneider, G Schneider, Collectionof bioactive reference compounds for focused library de-sign, QSAR & Combinatorial Science, 2003, 22, 713 – 718).

The 10 923 citations come from a total of 7130 publica-tions (i.e., some contain multiple citations to QSAR), andthe 20 journals citing QSAR most frequently are shown inTable3. There will be few surprises here; what is perhapssurprising is that there are no less than 1028 other journals

that have cited QSAR at least once during the period un-der review. These journals come from 151 different WOKSubject Categories, with the 31 singly-citing Categoriesrepresenting disciplines as diverse as Clinical Psychology,Geology, Linguistics, Optics, and Remote Sensing.

3.2 Subject Coverage

The journal�s own descriptions of its subject coverage havebeen noted in the Introduction, and these descriptions arereflected in the most-cited papers listed in Table 2. Thereare four (often overlapping) main types of topic discussed

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Table 2. The 20 most cited papers from the journal.

Authors Title Year Volume Pages Citations

R. D. Cramer et al. Cross-validation, bootstrapping, and partial least-squares compared with multiple-regression inconventional QSAR studies

1988 7 18 – 25 452

M. Baroni et al. Generating optimal linear pls estimations (GOLPE) –an advanced chemometric tool for handling3D-QSAR problems

1993 12 9 – 20 277

G. Klopman A hierarchical computer automated structure evaluationprogram .1.

1992 11 176 – 184 190

L. B. Kier A shape index from molecular graphs 1985 4 109 – 116 161M. Clark, R. D. Cramer The probability of chance correlation using partial least-

squares (PLS)1993 12 137 – 145 156

H. Kubinyi Variable selection in QSAR studies. 1. An evolutionaryalgorithm

1994 13 285 – 294 150

A. Avdeef Ph-metric log-p. 1. Difference plots for determining ion-pairoctanol-water partition-coefficients ofmultiprotic substances

1992 11 510 – 517 112

L. B. Kier Shape indexes of orders one and 3 from molecular graphs 1986 5 1 – 7 107P. Willett, V. Winterman A comparison of some measures for the determination of

intermolecular structural similaritymeasures of intermolecular structural similarity

1986 5 18 – 25 100

H. van de Waterbeemd et al. Estimation of Caco-2 cell permeability using calculatedmolecular descriptors

1996 15 480 – 490 99

H. Kubinyi Variable selection in QSAR studies. 2. A highly efficientcombination of systematic search andevolution

1994 13 393 – 401 97

L. H. Hall et al. The electrotopological state - an atom index for QSAR 1991 10 43 – 51 96R. Todeschini, P. Gramatica 3D-modelling and prediction by WHIM descriptors. 5.

Theory development and chemical meaningof WHIM descriptors

1997 16 113 – 119 93

S. H. Hilal et al. A rigorous test for SPARC�s chemical reactivity models:Estimation of more than 4300 ionizationpK(a)s

1995 14 348 – 355 91

B. Skagerberg et al. Principal properties for aromatic substituents – a multi-variate approach for design in QSAR

1989 8 32 – 38 91

J. Jonsson et al. Multivariate parameterization of 55 coded and non-codedamino-acids

1989 8 204 – 209 90

R. Mannhold, K. Dross Calculation procedures for molecular lipophilicity: Acomparative study

1996 15 403 – 409 82

G. D. Veith, O. Mekenyan A QSAR approach for estimating the aquatic toxicity ofsoft electrophiles

1993 12 349 – 356 75

J. D. Holliday et al. A fast algorithm for selecting sets of dissimilar moleculesfrom large chemical databases

1995 14 501 – 506 74

M. J. Kamlet et al. Solubility properties in biological media. 12. Regarding themechanism of nonspecific toxicity ornarcosis by organic nonelectrolytes

1988 7 71 – 78 70

Bibliometric Study of QSAR

in these articles: multivariate statistics (e.g., Cramer et al.,1988; Kubinyi, 1994); QSAR techniques (e.g., Baroniet al., 1993; Kier, 1985); the prediction of specific types ofproperty (e.g., van de Waterbeemd et al., 1996; Avdeef,1992); and chemoinformatics techniques that are moregeneral in application than just QSAR (e.g., Klopman;Willett & Winterman, 1986).

Missing from Table 2 are articles describing applicationsof established QSAR methods to particular sets of mole-cules and their associated activities: there are many suchpublications in the journal but their specific chemical andbiological foci means that they are generally less citedthan methodological articles of more widespread applica-bility. The importance of applications to the discipline is,however, obvious if one considers their representation inthe journal over the years. Inspection of the titles of thepapers published in Volumes 4, 14 and 24 (excluding com-binatorial science papers and special issues in the case ofVolume 24) shows that in each year around 60% of the pa-pers had an application focus (as reflected by mention inthe title of a specific class of structures and/or of a specifictype of biological activity), and this focus is further reflect-ed in one of the important features of the journal. This isthe section “Abstracts of publications related to QSAR”,which commenced in Volume 7 when it was organized intwelve subsections: Review; Correlation Analysis: Theo-retical Papers; Correlation Analysis, Application: Pharma-cology; Correlation Analysis, Application: AgriculturalChemistry; Correlation Analysis, Application: Physical Or-ganic Chemistry; Correlation Analysis, Application: Chro-matography; Correlation Analysis, Application: Environ-mental Sciences; Correlation Analysis, Other Applica-

tions: Toxicology, Chemotherapy, Pharmacokinetics, Me-tabolism; Multivariate Analysis, Pattern Recognition, Ex-perimental Design; Molecular Graphics, MolecularMechanics and Quantum Chemistry; Artificial Intelli-gence, Expert Systems. Thus, one-half of the subsectionsare given over to reports of applications and this focus hascontinued to the present-day, albeit with changes else-where. By Volume 11, part 2, the various CorrelationAnalysis subsections had been replaced by analogous Clas-sical QSAR subheadings, the use of “Classical” arisingfrom the arrival of the new, and increasingly popular, sub-section 3-D QSAR. There were by now also subsectionsfor Proteins and Peptides and for Computer Algorithm,Software; and the Multivariate Analysis subsection by nowincluded Neural Networks and Similarity Analysis.Changes since then have been less marked, the most re-cent additions being the appearance in 2004 of ADME (asone of the Classical QSAR subsections) and of VirtualScreening (as an adjunct to the Data Base subsection thathad replaced the earlier subsection Computer Algorithm,Software).

3.3 Relationship to other Journals

There are several other journals that cover similar groundto QSAR, and these topic relationships are explored in de-tail in the JCR database. Specifically, Table 4 lists the relat-edness, R, values from the JCR for the 2008 issues ofQSAR & Combinatorial Science. R is determined for someperiod of time using a calculation that is based on threefactors: the number of citations in that period from QSAR& Combinatorial Science (in this particular case) to anoth-

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Table 3. The 20 journals that cite QSAR most frequently. Values for starred (*) journals include citations from that journal under aprevious name.

Journal title Number of citations

Journal of Chemical Information and Modeling (*) 559QSAR & Combinatorial Science (*) 476Journal of Medicinal Chemistry 403Journal of Computer-Aided Molecular Design (*) 261Bioorganic & Medicinal Chemistry 216SAR and QSAR in Environmental Research 150European Journal of Medicinal Chemistry 132Chemosphere 92Environmental Toxicology and Chemistry 77Journal of Molecular Graphics & Modelling (*) 76Analytica Chimica Acta 74Journal of Molecular Structure-Theochem 74Bioorganic & Medicinal Chemistry Letters 70Chemometrics and Intelligent Laboratory Systems 70Journal of Pharmaceutical Sciences 70Journal of Chromatography A 60International Journal of Quantum Chemistry 53Journal of Organic Chemistry 53Chemical Research in Toxicology 52Journal of Chemometrics 51

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er journal, J; the total number of citations from QSAR &Combinatorial Science to all journals; and the total num-ber of articles in the other journal, J [19]. Let A and B betwo journals publishing PA and PB articles, respectively.Then let CAB (or CBA) be the number of times that A citesB (or that B cites A), and let CTA (or CTB) be the totalnumber of citations in A (or B). The relatedness of journalA to journal B, RAB, is defined as

CAB/(PB�CTA)

and similarly for RBA. Table 4 lists the 20 most closely re-lated journals, ranked in descending order of the larger ofthe two possible R values (using citations from QSAR &Combinatorial Science to J; and using citations from J toQSAR & Combinatorial Science) in each case, as suggest-ed by Pudovkin and Garfield [19]. Using the relatednessmeasure, the closest journal to QSAR & CombinatorialScience is SAR & QSAR in Environmental Research, andconsideration of the relatedness values for SAR & QSARin Environmental Research (data not shown) demonstratesthat this relationship is a reciprocal one in that QSAR &Combinatorial Science is its closest journal. It will be seenfrom Table 4 that some of the pairs of relateness valuesare markedly different, e.g., QSAR & Combinatorial Sci-ence is far more closely related to Journal of Computation-al Chemistry than that journal is to QSAR & Combinatori-al Science; ATLA – Alternatives to Laboratory Animals isan example of a journal where the relationship is skewedin the opposite direction.

The overlap between two journals can also be studiedby looking at the extent to which authors publish in bothjournals. A recent bibliometric analysis listed the mostproductive authors in a range of chemoinformatics jour-nals [14], and a comparison of these lists with the mostproductive QSAR authors noted previously in the Section“Analysis of publications and citations” demonstratessome degree of overlap: specifically, Fan appears in thelists for Journal of Chemical Information and Modeling,Journal of Computer-Aided Molecular Design and SARand QSAR in Environmental Science; and both Croninand Mekenyan appear in the list for SAR and QSAR inEnvironmental Research. Other productive QSAR authorsinclude: Dearden (8 articles), who appears in the list forSAR and QSAR in Environmental Research; Willett (8 ar-ticles), who appears in the lists for Journal of Chemical In-formation and Modeling, Journal of Computer-Aided Mo-lecular Design and Journal of Molecular Graphics andModelling; Jurs (5 articles), who appears in the list forJournal of Chemical Information and Modeling; Gasteiger(4 articles), who appears in the lists for Journal of Chemi-cal Information and Modeling and Journal of Computer-Aided Molecular Design; and Katritzky (4 articles), whoappears in the list for Journal of Chemical Information andModeling.

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Table 4. Relatedness values (from the Journal Citation Reports database) of the 20 journals most closely related to QSAR & Combi-natorial Science.

Journal title Relatedness

QSAR & CombinatorialScience to journal J

Journal J to QSAR &Combinatorial Science

SAR & QSAR in Environmental Research 417.7 184.2QSAR & Combinatorial Science 313.8 313.8Journal of Chemical Information and Modeling 283.0 57.7Journal of Computer-Aided Molecular Design 194.1 77.0ATLA – Alternatives to Laboratory Animals 109.4 22.5Chemometrics and Intelligent Laboratory Systems 105.2 46.8Journal of Molecular Graphics and Modelling 101.4 53.1Chemical Biology & Drug Design 4.1 72.4Journal of Chemometrics 63.1 10.1Journal of Computational Chemistry 47.9 6.3Chemical Reviews 47.9 2.7Medicinal Research Reviews 46.0 4.0Chemical Research in Toxicology 46.0 8.0Journal of Medicinal Chemistry 44.4 4.9Fluid Phase Equilibria 43.3 2.6Environmental Toxicology and Chemistry 37.1 7.3Thermochimica Acta 35.3 3.9Medicinal Chemistry Research 21.2 32.0Journal of Combinatorial Chemistry 8.7 26.7European Journal of Medicinal Chemistry 25.1 22.3

Bibliometric Study of QSAR

4 Conclusions

For 28 years, Quantitative Structure-Activity Relationshipsand then QSAR & Combinatorial Science have publishedmany of the key papers in the development of QSAR andrelated specialisms in computational chemistry. The mosthighly-cited papers are methodological in character, withseveral of these receiving in excess of one-hundred cita-tions to date. While the journal publishes many papers de-scribing the practical application of QSAR methods, thesepapers tend to be much less heavily cited. The journal isinternational in scope, receiving papers from around theworld, but with the USA being by far the largest source ofarticles. It is also wide-ranging in its impact, attracting cita-tions from journals in a very broad range of academic dis-ciplines.

5 References

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[4] H. Kubinyi, Quant. Struct.-Activ. Relat. 2002, 21, 348 – 356.[5] C. D. Selassie, in Burger�s Medicinal Chemistry and Drug

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