CAS Information Services for Medicinal Chemists

Jj, Heyman; Eh, Karasinskia; Pm, Giles

doi:10.1177/009286158201600406

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Hence, most chemical software tools support database searches employing molecule, substructure, or chemical pattern matching algorithms. With increasing size of molecular databases, conventional molecule and especially chemical pattern search − becomes demanding concerning storage and search time requirements. With some exceptions, e.g., the Chemical Universe Database GDB-13, which comprises around 970 million entries, molecular databases generally store only a few million compounds. , However, the size of the chemical space is estimated to be much larger.…”

Section: Introductionmentioning

confidence: 99%

Searching for Recursively Defined Generic Chemical Patterns in Nonenumerated Fragment Spaces

Ehrlich

Henzler

Rarey

2013

J. Chem. Inf. Model.

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Retrieving molecules with specific structural features is a fundamental requirement of today's molecular database technologies. Estimates claim the chemical space relevant for drug discovery to be around 10⁶⁰ molecules. This figure is many orders of magnitude larger than the amount of molecules conventional databases retain today and will store in the future. An elegant description of such a large chemical space is provided by the concept of fragment spaces. A fragment space comprises fragments that are molecules with open valences and describes rules how to connect these fragments to products. Due to the combinatorial nature of fragment spaces, a complete enumeration of its products is intractable. We present an algorithm to search fragment spaces for generic chemical patterns as present in the SMARTS chemical pattern language. Our method allows specification of the chemical surrounding of an atom in a query and, therefore, enables a chemically intuitive search. During the search, the costly enumeration of products is avoided. The result is a fragment space that exactly describes all possible molecules that contain the user-defined pattern. We evaluated the algorithm in three different drug development use-cases and performed a large scale statistical analysis with 738 SMARTS patterns on three public available fragment spaces. Our results show the ability of the algorithm to explore the chemical space around known active molecules, to analyze fragment spaces for the presence of likely toxic molecules, and to identify complex macromolecular structures under additional structural constraints. By searching the fragment space in its nonenumerated form, spaces covering up to 10¹⁹ molecules can be examined in times ranging between 47 s and 19 min depending on the complexity of the query pattern.

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Section: Introductionmentioning

confidence: 99%

Searching for Recursively Defined Generic Chemical Patterns in Nonenumerated Fragment Spaces

Ehrlich

Henzler

Rarey

2013

J. Chem. Inf. Model.

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“…Due to the large and still growing number of commercially available and synthetically accessible molecule structures, efficient algorithms for searching large data sets are becoming more and more vital. , Traditionally, huge compound sets are maintained in large databases. Different computational methods have been developed to efficiently search through these data sets. − One major application is the retrieval of molecules that include a defined molecular substructure.…”

Section: Introductionmentioning

confidence: 99%

Searching for Substructures in Fragment Spaces

Ehrlich

Volkamer

Rarey

2012

J. Chem. Inf. Model.

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A common task in drug development is the selection of compounds fulfilling specific structural features from a large data pool. While several methods that iteratively search through such data sets exist, their application is limited compared to the infinite character of molecular space. The introduction of the concept of fragment spaces (FSs), which are composed of molecular fragments and their connection rules, made the representation of large combinatorial data sets feasible. At the same time, search algorithms face the problem of structural features spanning over multiple fragments. Due to the combinatorial nature of FSs, an enumeration of all products is impossible. In order to overcome these time and storage issues, we present a method that is able to find substructures in FSs without explicit product enumeration. This is accomplished by splitting substructures into subsubstructures and mapping them onto fragments with respect to fragment connectivity rules. The method has been evaluated on three different drug discovery scenarios considering the exploration of a molecule class, the elaboration of decoration patterns for a molecular core, and the exhaustive query for peptides in FSs. FSs can be searched in seconds, and found products contain novel compounds not present in the PubChem database which may serve as hints for new lead structures.

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Substruktur‐Recherchen

Zaß¹

1984

Nachr. Chem. Tech. Lab.

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CAS Information Services for Medicinal Chemists

Cited by 4 publications

References 2 publications

Searching for Recursively Defined Generic Chemical Patterns in Nonenumerated Fragment Spaces

Searching for Recursively Defined Generic Chemical Patterns in Nonenumerated Fragment Spaces

Searching for Substructures in Fragment Spaces

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