Chemical Similarity Searching

Willett, Peter; and, John M. Barnard; Downs, Geoffrey M.

doi:10.1021/ci9800211

Cited by 1,636 publications

(1,687 citation statements)

References 83 publications

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“…The Tanimoto coefficient was used for all similarity calculations. 20 If two molecules have a and b bits set to "on" in their bit-strings with c bits in common, the Tanimoto similarity is defined to be (1) Relating Ligand Sets through the Similarity Ensemble Approach 4 (SEA)…”

Section: Similarity Measuresmentioning

confidence: 99%

Quantifying the Relationships among Drug Classes

Hert

Keiser

Irwin

et al. 2008

J. Chem. Inf. Model.

152

165

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The similarity of drug targets is typically measured using sequence or structural information. Here, we consider chemo-centric approaches that measure target similarity on the basis of their ligands, asking how chemoinformatics similarities differ from those derived bioinformatically, how stable the ligand networks are to changes in chemoinformatics metrics, and which network is the most reliable for prediction of pharmacology. We calculated the similarities between hundreds of drug targets and their ligands and mapped the relationship between them in a formal network. Bioinformatics networks were based on the BLAST similarity between sequences, while chemoinformatics networks were based on the ligand-set similarities calculated with either the Similarity Ensemble Approach (SEA) or a method derived from Bayesian statistics. By multiple criteria, bioinformatics and chemoinformatics networks differed substantially, and only occasionally did a high sequence similarity correspond to a high ligand-set similarity. In contrast, the chemoinformatics networks were stable to the method used to calculate the ligand-set similarities and to the chemical representation of the ligands. Also, the chemoinformatics networks were more natural and more organized, by network theory, than their bioinformatics counterparts: ligand-based networks were found to be small-world and broad-scale.

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Section: Similarity Measuresmentioning

confidence: 99%

Quantifying the Relationships among Drug Classes

Hert

Keiser

Irwin

et al. 2008

J. Chem. Inf. Model.

152

165

View full text Add to dashboard Cite

show abstract

“…Herein, Tanimoto coefficient (Willett, Barnard and Downs, 1998) was used to evaluate the similarity of pathway fingerprint. Given a drug pair A and B, the Tanimoto coefficient for binary vectors was defined as formula (I)…”

Section: Calculating the Similarity Of Pathway Fingerprint Between Drugsmentioning

confidence: 99%

Study of drug function based on similarity of pathway fingerprint

et al. 2012

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Drugs sharing similar therapeutic function may not bind to the same group of targets. However, their targets may be involved in similar pathway profiles which are associated with certain pathological process. In this study, pathway fingerprint was introduced to indicate the profile of significant pathways being influenced by the targets of drugs. Then drug−drug network was further constructed based on significant similarity of pathway fingerprints. In this way, the functions of a drug may be hinted by the enriched therapeutic functions of its neighboring drugs. In the test of 911 FDA approved drugs with more than one known target, 471 drugs could be connected into networks. 760 significant associations of drug−therapeutic function were generated, among which around 60% of them were supported by scientific literatures or ATC codes of drug functional classification. Therefore, pathway fingerprints may be useful to further study on the potential function of known drugs, or the unknown function of new drugs.

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“…The similarity S XY between two fragment vectors X and Y was computed in all cases using the full form of the Tanimoto coefficient [9].…”

Section: Similarity Coefficientmentioning

confidence: 99%

“…A 2D fingerprint is a vector that encodes the presence or absence of topological substructures (typically atom-, bond-or ring-centred fragments) in a molecule [7,8], and many different types of fingerprint have been described in the literature [9,10]. A fingerprint is clearly an extremely simple type of structural representation, but still contains sufficient information to enable effective similarity-based virtual screening to be carried out (see, e.g., [11][12][13][14][15][16][17]).…”

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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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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Chemical Similarity Searching

Cited by 1,636 publications

References 83 publications

Quantifying the Relationships among Drug Classes

Quantifying the Relationships among Drug Classes

Study of drug function based on similarity of pathway fingerprint

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

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