A Modification of the Jaccard–Tanimoto Similarity Index for Diverse Selection of Chemical Compounds Using Binary Strings

Fligner, Michael A.; Verducci, Joseph S.; Blower, Paul E.

doi:10.1198/004017002317375064

Cited by 123 publications

(129 citation statements)

References 8 publications

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“…Although the Tanimoto coefficient is widely used, Flower has noted that it typically yields low similarity values when the reference molecule in a similarity search has just a few bits set in its fingerprint [52]. This marked sizedependency was confirmed in later studies [53][54][55], and it has also been shown that the coefficient has an inherent bias towards certain similarity values [37]. These observations were the starting point for our comparative studies of similarity coefficients, as described below.…”

Section: Comparison Of Similarity Coefficientsmentioning

confidence: 96%

Similarity-based virtual screening using 2D fingerprints

Willett

2006

Drug Discovery Today

770

673

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Teaser This paper discusses the use of binary-encoded fragment substructures to scan databases to find molecules that are structurally similar to a bioactive query compound.Abstract This paper summarises recent work at the University of Sheffield on virtual screening methods that use 2D fingerprint measures of structural similarity. A detailed comparison of a large number of similarity coefficients demonstrates that the well-known Tanimoto coefficient remains the method of choice for the computation of fingerprintbased similarity, despite possessing some inherent biases related to the sizes of the molecules that are being sought. Group fusion involves combining the results of similarity searches based on multiple reference structures and a single similarity measure. We demonstrate the effectiveness of this approach to screening, and also describe an approximate form of group fusion, turbo similarity searching, that can be used when just a single reference structure is available.

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Section: Comparison Of Similarity Coefficientsmentioning

confidence: 96%

Similarity-based virtual screening using 2D fingerprints

Willett

2006

Drug Discovery Today

770

673

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“…However, the Tanimoto coefficient has a known bias toward the retrieval of simplistic structures in diversity-based selection procedures. 39 This is because for any binary fingerprint there may exist more than one (specifically, 2 nÀa ) distinct, and maximally dissimilar, fingerprint. This effect can be mitigated by considering, in addition to the common presence of features, f, the common absence thereof.…”

Section: Articlementioning

confidence: 99%

“…This effect can be mitigated by considering, in addition to the common presence of features, f, the common absence thereof. 39 The modified Tanimoto system with this functionality devised by Fligner et al 39 is referred to here as the binary modified Tanimoto with weighting, MTW bin :…”

Section: Articlementioning

confidence: 99%

Addressing Challenges of Identifying Geometrically Diverse Sets of Crystalline Porous Materials

Martin

Smit

Harańczyk

2011

J. Chem. Inf. Model.

210

214

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“…The machine-derived, or spectral, descriptors are typically obtained in a combinatorial way, by indexing all of the possible labeled subgraphs of a given kind (e.g., paths, trees) and size (e.g., depth up to 3) of the molecular graphs, yielding the well-known fingerprint vector representations. [3][4][5][6][7][8][9] The somewhat parallel expansions in data and representations create new opportunities and challenges and require the continued development of methods to store and search molecules in chemical databases in ways that can scale up with these expansions. One strategy 10 to speed up database searches based on molecular similarity is essentially a pruning strategy with two basic components.…”

Section: Introductionmentioning

confidence: 99%

Speeding Up Chemical Database Searches Using a Proximity Filter Based on the Logical Exclusive OR

Baldi

Hirschberg

Nasr

2008

J. Chem. Inf. Model.

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In many large chemoinformatics database systems, molecules are represented by long binary fingerprint vectors whose components record the presence or absence in the molecular graphs of particular functional groups or combinatorial features, such as labeled paths or labeled trees. To speed up database searches, we propose to store with each fingerprint a small header vector containing primarily the result of applying the logical exclusive OR (XOR) operator to the fingerprint vector after modulo wrapping to a smaller number of bits, such as 128 bits. From the XOR headers of two molecules, tight bounds on the intersection and union of their fingerprint vectors can be rapidly obtained, yielding tight bounds on derived similarity measures, such as the Tanimoto measure. During a database search, every time these bounds are unfavorable, the corresponding molecule can be rapidly discarded with no need for further inspection. We derive probabilistic models that allow us to estimate precisely the behavior of the XOR headers and the level of pruning under different conditions in terms of similarity threshold and fingerprint density. These theoretical results are corroborated by experimental results on a large set of molecules. For a Tanimoto threshold of 0.5 (respectively 0.9), this approach requires searching less than 50% (respectively 10%) of the database, leading to typical search speedups of 2 to 3 times over the previous state-of-the-art.

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A Modification of the Jaccard–Tanimoto Similarity Index for Diverse Selection of Chemical Compounds Using Binary Strings

Cited by 123 publications

References 8 publications

Similarity-based virtual screening using 2D fingerprints

Similarity-based virtual screening using 2D fingerprints

Addressing Challenges of Identifying Geometrically Diverse Sets of Crystalline Porous Materials

Speeding Up Chemical Database Searches Using a Proximity Filter Based on the Logical Exclusive OR

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