Clustering Compound Data: Asymmetric Clustering of Chemical Datasets

MacCuish, Norah E.; MacCuish, John

doi:10.1021/bk-2005-0894.ch011

Cited by 5 publications

(6 citation statements)

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“…The use of the asymmetric Tversky measures (in essence a parameterized Tanimoto-like measure) has been explored in both similarity searching 20,21 andclustering. 22 The newer clustering applications of shape-based and shape-pharmacophore-based features use analogous measures such as the Tanimoto and Tversky. The alignment-dependent, Grant-Pickup 23 mixture of Gaussians, shape model uses a Shape-Tanimoto and, in a pharmacophore extension of this model, a color-Tanimoto measure.…”

Section: Measures Of Similaritymentioning

confidence: 99%

“…Asymmetric measures are less common as are the corresponding asymmetric clustering algorithms. The use of the asymmetric Tversky measures (in essence a parameterized Tanimoto‐like measure) has been explored in both similarity searching andclustering . The newer clustering applications of shape‐based and shape‐pharmacophore‐based features use analogous measures such as the Tanimoto and Tversky.…”

Section: Current Advancesmentioning

confidence: 99%

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Chemoinformatics applications of cluster analysis

MacCuish

2013

WIREs Comput Mol Sci

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Chemoinformatics applications of cluster analysis over the past 35 years include chemical diversity for compound acquisition, analysis of HTS results for lead discovery, 2D and 3D chemical similarity searching for virtual screening, and hypothesis generation for lead hopping using molecular shape and pharmacophore descriptors. These applications still provide the majority of cluster analysis usage, but the advent of greater and greater computational resources has allowed researchers to tackle applications of ever increasing scale and complexity. In the past few years, a far broader array of clustering methods is now used—some entirely new, some common to other disciplines, and others modified to specific chemoinformatic applications. The chemoinformatic applications have also broadened to include greater biological information more commonly associated with bioinformatics. Indeed, clustering techniques, such as coclustering or self‐organizing trees, commonly found in bioinformatics, are beginning to find chemoinformatic application uses. Issues such as visualization and validation of clustering results continue to present challenging problems, especially given that the scale of many problems now attempted has increased enormously. Some new validation techniques have been introduced in the chemoinformatics literature that now allow for both a better understanding of the clustering results and help point to methods of greater efficacy. Effective validation and visualization of clustering results of large data sets has proven to be more problematic. WIREs Comput Mol Sci 2014, 4:34–48. doi: 10.1002/wcms.1152 This article is categorized under: Computer and Information Science > Chemoinformatics

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Section: Measures Of Similaritymentioning

confidence: 99%

Section: Current Advancesmentioning

confidence: 99%

Chemoinformatics applications of cluster analysis

MacCuish

2013

WIREs Comput Mol Sci

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“…The files were converted to SMILES strings using Open Babel 17) . Bit-string data were generated from the SMILES strings using the Fingerprint Module of MESA 18) , which generated 164 bit-strings from SMILES strings input. The bit-strings are a public subset of 166 MACCS keys.…”

Section: Comparison Among Reaction Similarity Measuresmentioning

confidence: 99%

Reaction Similarities Focusing Substructure Changes of Chemical Compounds and Metabolic Pathway Alignments

Tohsato

Nishimura

2009

IPSJ Transactions on Bioinformatics

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Comparative analyses of enzymatic reactions provide important information on both evolution and potential pharmacological targets. Previously, we focused on the structural formulae of compounds, and proposed a method to calculate enzymatic similarities based on these formulae. However, with the proposed method it is difficult to measure the reaction similarity when the formulae of the compounds constituting each reaction are completely different. The present study was performed to extract substructures that change within chemical compounds using the RPAIR data in KEGG. Two approaches were applied to measure the similarity between the extracted substructures: a fingerprint-based approach using the MACCS key and the Tanimoto/Jaccard coefficients; and the Topological Fragment Spectra-based approach that does not require any predefined list of substructures. Whether the similarity measures can detect similarity between enzymatic reactions was evaluated. Using one of the similarity measures, metabolic pathways in Escherichia coli were aligned to confirm the effectiveness of the method.

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“…Bit-string data were generated from SMILIES data in the PubChem database using the Fingerprint Module of MESA in OpenEye Scientific Software 12) , which generated 164 bit-strings from SMILES strings input 13) . The bit-strings are a public subset of 166 MACCS keys.…”

Section: Experimental Datamentioning

confidence: 99%

Metabolic Pathway Alignment Based on Similarity between Chemical Structures

Tohsato

Nishimura

2007

ipsjdc

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In many of the chemical reactions in living cells, enzymes act as catalysts in the conversion of certain compounds (substrates) into other compounds (products). Metabolic pathways are formed as the products of these reactions are used as the substrates of other reactions. Comparative analyses of the metabolic pathways among species provide important information on both evolution and potential pharmacological targets. Here, we propose a method to align the metabolic pathways based on similarities between chemical structures. To measure the degree of chemical similarity, we formalized a scoring system using the MACCS keys and the Tanimoto/Jaccard coefficients. To determine the effectiveness of our method, it was applied to analyses of metabolic pathways in Escherichia coli. The results revealed compound similarities between fructose and mannose biosynthesis and galactose biosynthesis pathways.

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Clustering Compound Data: Asymmetric Clustering of Chemical Datasets

Cited by 5 publications

References 0 publications

Chemoinformatics applications of cluster analysis

Chemoinformatics applications of cluster analysis

Reaction Similarities Focusing Substructure Changes of Chemical Compounds and Metabolic Pathway Alignments

Metabolic Pathway Alignment Based on Similarity between Chemical Structures

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