Molecular similarity and diversity in chemoinformatics: From theory to applications

Maldonado, Ana G.; Doucet, Jean‐Pierre; Petitjean, Michel; Fan, Botao

doi:10.1007/s11030-006-8697-1

Cited by 222 publications

(123 citation statements)

References 255 publications

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“…One fundamental research theme in chemical database analysis is diversity of sampling [31]. The diversity problem involves defining a diverse subset of representative compounds.…”

Section: Diversity Analysismentioning

confidence: 99%

Quantitative structure-retention relationship prediction of Kováts retention index of some organic acids

Fatemi

Elyasi

2013

Acta Chromatographica

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Summary.In this work, quantitative structure-retention relationship (QSRR) approaches were applied for modeling and prediction of the gas chromatographic retention indices of some amino acids (AAs) and carboxylic acids (CAs). The genetic algorithm (GA) method was used to select the most relevant descriptors, which are responsible for the retention of these compounds. Then, multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM) were utilized to construct the nonlinear and linear quantitative structure-retention relationship models. The obtained results revealed that the GA-ANN developed model was better than other models. This model has the average absolute relative errors of 0.043, 0.052 and 0.045 for training, internal and external test set. Applying the 10-fold cross-validation procedure on GA-AAN model obtained the statistics of Q 2 = 0.941 which revealed the reliability of this model.

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“…One fundamental research theme in chemical database analysis is diversity of sampling [31]. The diversity problem involves defining a diverse subset of representative compounds.…”

Section: Diversity Analysismentioning

confidence: 99%

Quantitative structure-retention relationship prediction of Kováts retention index of some organic acids

Fatemi

Elyasi

2013

Acta Chromatographica

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“…Many techniques for molecular diversity analysis have been described [81][82][83]; here we focus on those that make use of fingerprint-based calculations of inter-molecular dissimilarity (where the dissimilarity is normally the complement of the Tanimoto similarity). Similarity and dissimilarity are properties of a pair of molecules, whereas diversity is the property of a set of molecules (either an entire database or a subset thereof) and is computed by combining sets of pair-wise similarities or dissimilarities.…”

Section: Molecular Diversity 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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“…The fundamental research themes in chemical database analysis are diversity of sampling [45]. The diversity problem involves defining a different subset of representative compounds.…”

Section: Molecular Diversity Validationmentioning

confidence: 99%

Prediction of n-octanol-water partition coefficient for polychlorinated biphenyls from theoretical molecular descriptors

Safdari

Golmohammadi

2010

Eur. J. Chem.

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A quantitative structure-property relationship (QSPR) study was performed to develop models that relate the structures of 133 polychlorinated biphenyls to their n-octanol-water partition coefficients (log Kow). Molecular descriptors were derived solely from 3D structures of the molecules. The genetic algorithm-partial least squares (GA-PLS) method was applied as a variable selection tool. The partial least square (PLS) method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network model.

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Molecular similarity and diversity in chemoinformatics: From theory to applications

Cited by 222 publications

References 255 publications

Quantitative structure-retention relationship prediction of Kováts retention index of some organic acids

Quantitative structure-retention relationship prediction of Kováts retention index of some organic acids

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

Prediction of n-octanol-water partition coefficient for polychlorinated biphenyls from theoretical molecular descriptors

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