Similarity Searching and Clustering of Chemical-Structure Databases Using Molecular Property Data

Downs, Geoffrey M.; Willett, Peter; Fisanick, William

doi:10.1021/ci00021a011

Cited by 150 publications

(102 citation statements)

References 7 publications

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“…This has encouraged comparative studies to determine the effectiveness of the various clustering methods that are available when they are applied to the clustering of chemical structures. Early property-prediction studies of over 30 hierarchic and non-hierarchic clustering methods [4] highlighted the consistent performance of the hierarchical agglomerative method first described by Ward [12], a finding that was confirmed in subsequent studies by Brown and Martin [10; 13] and by Downs et al [14].…”

Section: Introductionmentioning

confidence: 81%

Clustering files of chemical structures using the Székely–Rizzo generalization of Ward's method

Varin

Bureau

Mueller

et al. 2009

Journal of Molecular Graphics and Modelling

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Ward's method is extensively used for clustering chemical structures represented by 2D fingerprints. This paper compares Ward clusterings of 14 datasets (containing between 278 and 4332 molecules) with those obtained using the Székely-Rizzo clustering method, a generalization of Ward's method. The clusters resulting from these two methods were evaluated by the extent to which the various classifications were able to group active molecules together, using a novel criterion of clustering effectiveness. Analysis of a total of 1400 classifications (Ward and Székely-Rizzo clustering methods, fourteen different datasets, five different fingerprints and ten different distance coefficients) demonstrated the general superiority of the Székely-Rizzo method. The distance coefficient first described by Soergel performed extremely well in these experiments, and this was also the case when it was used in simulated virtual screening experiments.

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Section: Introductionmentioning

confidence: 81%

Clustering files of chemical structures using the Székely–Rizzo generalization of Ward's method

Varin

Bureau

Mueller

et al. 2009

Journal of Molecular Graphics and Modelling

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“…[16][17][18] 32. co-drug mutual prodrug [1] Two chemically linked synergistic drugs [1] designed to improve the drug delivery properties of one or both drugs.…”

Section: Notementioning

confidence: 99%

Glossary of terms used in medicinal chemistry. Part II (IUPAC Recommendations 2013)

Buckle¹,

Erhardt

Ganellin

et al. 2013

Pure and Applied Chemistry

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The evolution that has taken place in medicinal chemistry practice as a result of major advances in genomics and molecular biology arising from the Human Genome Project has carried with it an extensive additional working vocabulary that has become both integrated and essential terminology for the medicinal chemist. Some of this augmented terminology has been adopted from the many related and interlocked scientific disciplines with which the modern medicinal chemist must be conversant, but many other terms have been introduced to define new concepts and ideas as they have arisen. In this supplementary Glossary, we have attempted to collate and define many of the additional terms that are now considered to be essential components of the medicinal chemist's expanded repertoire.

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“…Jarvis-Patrick is by far the more efficient of these two methods, and it was hence the method of choice for clustering large chemical datasets for many years. However, improvements in both hardware and software, coupled with further demonstrations of the greater effectiveness of Ward's method [13,75] mean that this has now largely replaced the Jarvis-Patrick method for clustering databases containing up to ca. half-a-million structures.…”

Section: Cluster Analysismentioning

confidence: 99%

Similarity‐based data mining in files of two‐dimensional chemical structures using fingerprint measures of molecular resemblance

Willett

2011

WIREs Data Min & Knowl

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This paper reviews the use of measures of inter-molecular similarity for processing databases of chemical structures, which play an important role in the discovery of new drugs by the pharmaceutical industry. The similarity measures considered here are based on the use of a fingerprint representation of molecular structure, where a fingerprint is a vector encoding the presence of fragment substructures in a molecule and where the similarity between pairs of such fingerprints is computed using an association coefficient such as the Tanimoto coefficient. The Similar Property Principle provides the basic rationale for the use of similarity methods in three important chemoinformatics applications: similarity searching, database clustering, and molecular diversity analysis. Similarity searching enables the identification of those molecules in a database that are most similar to a userdefined, biologically active query molecule, with data fusion providing an effective way of combining the results of multiple similarity searches. Cluster analysis, typically using the Jarvis-Patrick, Ward or divisive k-means clustering methods, enables the cost-effective selection of molecules for biological testing, for property prediction and for investigating database overlap. Molecular diversity analysis, typically using cluster-based, dissimilarity-based or optimisation-based approaches, enables the identification of structurally diverse sets of molecules, so as to ensure that the full chemical space spanned by a database is tested in the search for novel bioactive molecules.

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Similarity Searching and Clustering of Chemical-Structure Databases Using Molecular Property Data

Cited by 150 publications

References 7 publications

Clustering files of chemical structures using the Székely–Rizzo generalization of Ward's method

Clustering files of chemical structures using the Székely–Rizzo generalization of Ward's method

Glossary of terms used in medicinal chemistry. Part II (IUPAC Recommendations 2013)

Similarity‐based data mining in files of two‐dimensional chemical structures using fingerprint measures of molecular resemblance

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