Evaluation of Similarity Measures for Searching the <i>Dictionary of Natural Products</i> Database

Whittle, Martin; Willett, Peter; Klaffke, Werner; Noort, Paula van

doi:10.1021/ci025591m

Cited by 59 publications

(76 citation statements)

References 26 publications

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“…The appropriate form of the Dice coefficient would be (A.3) which returns a value of 8/6, i.e., a spurious result greater than perfect similarity while equation (1) gives a value of 1. Although a working program can be achieved using the Dice coefficient in this context (earlier versions of our routine used it), previous experience has shown that it can be unwise to use a coefficient that returns similarity values outside of the prescribed range (Whittle, Willett, Klaffke, & van-Noort, 2003). It is in any case mathematically uncomfortable, and we therefore use the alternative formulation as described.…”

Section: Discussionmentioning

confidence: 99%

Data mining of search engine logs

Whittle

Eaglestone

Ford

et al. 2007

J. Am. Soc. Inf. Sci.

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This article reports on the development of a novel method for the analysis of Web logs. The method uses techniques that look for similarities between queries and identify sequences of "query transformation". It allows sequences of query transformations to be represented as graphical networks, thereby giving a richer view of search behavior than is possible with the usual sequential descriptions. We also perform a basic analysis to study the correlations between observed transformation codes, with results that appear to show evidence of behavior habits. The method was developed using transaction logs from the Excite search engine to provide a tool for an ongoing research project that is endeavoring to develop a greater understanding of Web-based searching by the general public. IntroductionThe research reported here is part of a larger experimental project designed to develop an understanding of howand how effectively-the general public search for information on the Web. The long-term aim of this study is to build an evidence-based model of effective searching that should inform the design of training and intelligent adaptive search interfaces. The evidence involved will include audio transcriptions, cognitive style scores, background questionnaire results, and search appraisal scores from our volunteer subjects as well as an analysis of their query transformation patterns. Query transformations are defined here as the linguistic and conceptual strategic changes searchers make as they repeatedly reformulate their search queries in response to ongoing failure and success. The rationale of the current project is based on the hypothesis that by studying query transformations in relation to other data, we can gain insights into what are the essential generic strategies that may result in more, and less, effective Web searching.In this article, we focus on the development of methods for the extraction and analysis of query transformation types and, to this end, an analysis has been conducted using transaction logs from the Excite search engine dating from 2001. Although these are not current, they represent a typical record of queries from that time period with many of the syntactic changes that will be of interest to us in our empirical studies. They are, moreover, well known and extensively studied (Spink, Wolfram, Jansen, & Saracevic, 2001). Although the lack of context places a limit on their usefulness, search logs do provide abundant data relating to syntactic changes and form an excellent training ground for the development of our analytical methods. The resulting analysis can be applied to our own empirical search data and used in conjunction with qualitative results to aid the development of our model. Related WorkA significant body of research into Web searching now exists (Spink & Jansen, 2004). The motivation for such studies includes the exploitation of generic knowledge of effective searching to provide better training and the development of intelligent search engines that will proactively assist the sear...

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Section: Discussionmentioning

confidence: 99%

Data mining of search engine logs

Whittle

Eaglestone

Ford

et al. 2007

J. Am. Soc. Inf. Sci.

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“…Various molecular representations in combination with different similarity coefficients that are usually used for similarity searching in synthetic compounds databases have been assessed for their effectiveness in searching databases of natural products [254]. In that study, the Russell-Rao coefficient and Unity fingerprints appeared to be the best combination for large molecules.…”

Section: Natural Products and Diversity-oriented Synthesismentioning

confidence: 99%

Diversity in Medicinal Chemistry Space

Gorse¹

2006

CTMC

108

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The chemical universe containing organic molecules within a reasonable molecular weight is vast and largely unexplored. Estimations of possible numbers of unique molecules range from 10(13) to 10(180). These numbers have to be compared with the few tens of millions of compounds currently known. Design of libraries that populate the medicinally relevant chemical subspace and tools that help to maximise the chance of identifying leads are necessary. This review describes various molecular representations that lead to the definition of chemical space, drug space or activity space. Strategies for compound selection in such spaces are discussed, as well as potential sources of diversity that could be used to explore the medicinal space in quest of new drugs.

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“…In addition, useful data fusion approaches could potentially result in a merged result that is superior to any of the individual input method results and thus is able to extract useful information from all input lists, including inferior methods. Among the published fusion methods, the Sumrank method has been shown to be among the most successful [145][146][147][148][149].…”

Section: Virtual Hit-lists: Data-fusion Vs Data-aggregationmentioning

confidence: 99%

Virtual Screening: Are We There Yet?

Jalaie

Shanmugasundaram²

2006

MRMC

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The cost of pharmaceutical development has increased dramatically in recent years, and many assorted approaches have been developed to decrease both the time and costs associated with bringing a drug to the market. Among these methods is the use of in silico screening of compound databases for potential new lead compounds, commonly referred to as virtual screening (VS). Virtual screening has become an integral part of the early discovery process in pharmaceutical development, readily observed by the large number of methodologies that have been published to date. Other reviews have been published detailing the various types of virtual screening methods in use. This work will review some of the virtual screening approaches and strategies that have been attempted to identify compounds to launch medicinal chemistry campaigns. Understanding trends and drivers in VS should help to set expectations about how and when VS could be used and what it can and cannot deliver and how it can be integrated in a successful screening campaign and used in a complementary fashion to HTS.

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Evaluation of Similarity Measures for Searching the Dictionary of Natural Products Database

Cited by 59 publications

References 26 publications

Data mining of search engine logs

Data mining of search engine logs

Diversity in Medicinal Chemistry Space

Virtual Screening: Are We There Yet?

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