Similarity methods in chemoinformatics

Willett, Peter

doi:10.1002/aris.2009.1440430108

Cited by 86 publications

(92 citation statements)

References 441 publications

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“…The Similar Property Principle [11,18,55] would lead us to expect that the active molecules in an activity class are likely to be more similar to an active reference structure from that class than are the inactive molecules (although there are, of course, many exceptions to this generalisation). Thus, if we plot the frequency distributions for the similarities between the reference structure and the set of active molecules, and the similarities between the reference structure and the set of inactive molecules (which we shall refer to as the Actives distribution and the Inactives distribution, respectively) then we would expect a plot such as that shown in Figure 2a, which is based on the M22 measure.…”

Section: Discussionmentioning

confidence: 99%

“…Here, the similarity is computed between a reference structure of known biological activity and each of the structures in a database; the most similar structures -the nearest neighbours -are then prime candidates for biological screening. The similarity is computed using a similarity coefficient, normally the Tanimoto coefficient, which is based on the substructures common to the fingerprints representing the reference structure and the current database structure [18].…”

Section: Introductionmentioning

confidence: 99%

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Analysis and use of fragment-occurrence data in similarity-based virtual screening

Arif

Holliday

Willett

2009

J Comput Aided Mol Des

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Current systems for similarity-based virtual screening use similarity measures in which all the fragments in a fingerprint contribute equally to the calculation of structural similarity. This paper discusses the weighting of fragments on the basis of their frequencies of occurrence in molecules. Extensive experiments with sets of active molecules from the MDL Drug Data Report and the World of Molecular Bioactivity databases, using fingerprints encoding Tripos holograms, Pipeline Pilot ECFC_4 circular substructures and Sunset Molecular keys, demonstrate clearly that frequency-based screening is generally more effective than conventional, unweighted screening. The results suggest that standardising the raw occurrence frequencies by taking the square root of the frequencies will maximise the effectiveness of virtual screening. An upper-bound analysis shows the complex interactions that can take place between representations, weighing schemes and similarity coefficients when similarity measures are computed, and provides a rationalisation of the relative performance of the various weighting schemes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis and use of fragment-occurrence data in similarity-based virtual screening

Arif

Holliday

Willett

2009

J Comput Aided Mol Des

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“…Similarity searching is one of the most popular forms of ligand-based virtual screening [13][14][15][16] and has been one of our principal research areas for many years. Indeed, the widespread use of 2D fingerprints and the Tanimoto coefficient for computing molecular similarity is arguably due in large part to one of the first operational systems for similarity searching that was developed in a Sheffield collaboration with Pfizer in the mid-Eighties.…”

Section: Similarity-based Virtual Screeningmentioning

confidence: 99%

Chemoinformatics at the University of Sheffield 2002–2014

Gillet

Holliday

Willett

2015

Molecular Informatics

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“…Many different types of similarity coefficient have been used in chemoinformatics, most commonly association coefficients such as the Tanimoto coefficient [24]. As noted above, the Székely-Rizzo method requires the use of a distance, and we have hence used the ten distance coefficients listed below, taken from the extensive review of metric coefficients presented by Gower and Legendre [25].…”

Section: Distance Coefficientsmentioning

confidence: 99%

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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Similarity methods in chemoinformatics

Cited by 86 publications

References 441 publications

Analysis and use of fragment-occurrence data in similarity-based virtual screening

Analysis and use of fragment-occurrence data in similarity-based virtual screening

Chemoinformatics at the University of Sheffield 2002–2014

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

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