Similarity searching on CAS Registry substances. 1. Global molecular property and generic atom triangle geometric searching

Fisanick, William; Cross, Kevin P.; Rusinko, Andrew

doi:10.1021/ci00010a013

Cited by 55 publications

(50 citation statements)

References 5 publications

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“…[13][14][15] The 2D studies involved subsets of the substructural fragments that comprise the CAS Online screen dictionary, 16 focusing on the different types of similarity relationships that can be identified between a target structure and a database structure when different classes of substructural fragment are employed. For example, the selection of augmented atoms (an atom and its pendant atoms and bonds) and atom sequences (unbranched chains of atoms) gives a very different view of the structural resemblances between a pair of molecules from that provided by the selection of ring composition fragments (the atoms within a ring and the bonds between them).…”

Section: Fragment-based Similarity Searchingmentioning

confidence: 99%

“…The "atom triangles" are atom triangles with generalized atom types, and the atom triangle three-slot and atom triangle fiVe-slot are reduced and generalized atom triangle descriptors using two atoms and one or three of the interatomic distances, respectively. The detailed search results provided by Fisanick et al 13 demonstrate that these triangle-based features provide a simple and effective mechanism for similarity searching based on size and shape.…”

Section: Structural Representations For Similarity Searchingmentioning

confidence: 99%

“…An alternative approach recognizes that different similarity measures reflect different types of molecular characteristic, and the multifaceted nature of biological activity would thus suggest that no single measure will be optimal for all sorts of similarity search that one might wish to carry out. Instead, one can use several different similarity measures for searching, as advocated by Fisanick et al in their work with different subsets of the CAS Online screen dictionary, 13 or even combine them into a new integrated measure. Examples of this latter approach are discussed by Sheridan et al, 56 by Kearsley et al, 58 and by Ginn et al, 99 who have all combined the rankings produced by different measures to give a single resultant ranking, an example of the more general procedure known as data fusion [100].…”

Section: Applications Of Similarity Searchingmentioning

confidence: 99%

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Chemical Similarity Searching

Willett

and

Downs

1998

J. Chem. Inf. Comput. Sci.

1,636

1,643

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This paper reviews the use of similarity searching in chemical databases. It begins by introducing the concept of similarity searching, differentiating it from the more common substructure searching, and then discusses the current generation of fragment-based measures that are used for searching chemical structure databases. The next sections focus upon two of the principal characteristics of a similarity measure: the coefficient that is used to quantify the degree of structural resemblance between pairs of molecules and the structural representations that are used to characterize molecules that are being compared in a similarity calculation. New types of similarity measure are then compared with current approaches, and examples are given of several applications that are related to similarity searching.

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Section: Fragment-based Similarity Searchingmentioning

confidence: 99%

Section: Structural Representations For Similarity Searchingmentioning

confidence: 99%

Section: Applications Of Similarity Searchingmentioning

confidence: 99%

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Chemical Similarity Searching

Willett

and

Downs

1998

J. Chem. Inf. Comput. Sci.

1,636

1,643

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“…Angle-descriptors are based on generalized valence angles and torsion angles. Descriptors named ™potential pharmacophore points∫ (PPP) are a generalized mix of distance and angle descriptors [28]. All the atoms of the molecule are analyzed to see whether they can be classified into one of the point types (H-bond donor, …”

Section: Distance-based and Angle-based Descriptorsmentioning

confidence: 99%

Approaches to Measure Chemical Similarity – a Review

2003

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Although the concept of similarity is a convenient for humans, a formal definition of similarity between chemical compounds is needed to enable automatic decision‐making. The objective of similarity measures in toxicology and drug design is to allow assessment of chemical activities. The ideal similarity measure should be relevant to the activity of interest. The relevance could be established by exploiting the knowledge about fundamental chemical and biological processes responsible for the activity. Unfortunately, this knowledge is rarely available and therefore different approximations have been developed based on similarity between structures or descriptor values. Various methods are reviewed, ranging from two‐dimensional, three‐dimensional and field approaches to recent methods based on “Atoms in Molecules” theory. All these methods attempt to describe chemical compounds by a set of numerical values and define some means for comparison between them. The review provides analysis of potential pitfalls of this methodology – loss of information in the representations of molecular structures – the relevance of a particular representation and chosen similarity measure to the activity. A brief review of known methods for descriptor selection is also provided. The popular “neighborhood behavior” principle is criticized, since proximity with respect to descriptors does not necessarily mean proximity with respect to activity. Structural similarity should also be used with care, as it does not always imply similar activity, as shown by examples. We remind that similarity measures and classification techniques based on distances rely on certain data distribution assumptions. If these assumptions are not satisfied for a given dataset, the results could be misleading. A discussion on similarity in descriptor space in the context of applicability domain assessment of QSAR models is also provided. Finally, it is shown that descriptor based similarity analysis is prone to errors if the relationship between the activity and the descriptors has not been previously established. A justification for the usage of a particular similarity measure should be provided for every specific activity by expert knowledge or derived by data modeling techniques.

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“…The shape of a molecule is often a key factor in determining whether a molecule will be bioactive, and many types of 3D representation have thus been reported in the similarity literature [7]. Examples include fingerprints that encode interatomic distance or angular information [100,101], and descriptions of molecular shape [102,103] and of the distribution of electrostatic charge around a molecule [104,105]. However, these all need to take account of the fact that most molecules are flexible, i.e., they can adopt several or many different 3D shapes (called conformations) (where as a molecule has only a single 2D topology).…”

Section: Resultsmentioning

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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Similarity searching on CAS Registry substances. 1. Global molecular property and generic atom triangle geometric searching

Cited by 55 publications

References 5 publications

Chemical Similarity Searching

Chemical Similarity Searching

Approaches to Measure Chemical Similarity – a Review

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

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