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Substructures MACCS

Unclear Magnetic Kesonance Spectroscopy. Bruker s database, designed for use with its spectrophotometers, contains 20,000 13C-nmr and Tl-nmr, as well as a combined nmr-ms database (66). Sadder Laboratories markets a PC-based system that can search its collection of 30,000 13C-nmr spectra by substructure as well as by peak assignments and by full spectrum (64). Other databases include one by Varian and a CD-ROM system containing polymer spectra produced by Tsukuba University, Japan. CSEARCH, a system developed at the University of Vienna by Robien, searches a database of almost 16,000 13C-nmr. Molecular Design Limited (MDL) has adapted the Robien database to be searched in the MACCS and ISIS graphical display and search environment (63). Projects are under way to link the MDL system with the Sadder library and its unique search capabilities. [Pg.121]

CATS3D was not only successful in scaffold hopping on the basis of the definition above (Meqi). We also observed a substructure hopping , which might be seen as an equivalent to more traditional bioisosteric replacement strategies. It seems that the CATS descriptor family represents molecules in a way that allows a combination of scaffold hopping and substructure hopping at once. This can result in a selection of molecules which would not be considered similar by other methods such as the MACCS keys. [Pg.71]

MACCS keys substructure descriptors (0 structural keys)... [Pg.475]

MACCS substructure keys on the other hand encode the presence of a predefined set of relevant 2D fragments, originally designed for speeding up database substructure searching [48,49] by eliminating those compounds from detailed consideration that can-... [Pg.413]

As it was previously shown that MACCS substructure keys outperform UNITY and Daylight 2D fingerprints [46], the IC93 database was investigated using an implementa-... [Pg.423]

Figure 13.6. Percent biological classes covered from the IC93 database versus subset sizes for maximum dissimilarity selections using selected MACCS substructure keys counting up to 1,3.5 or 9 occurrences of a particular fragment key, UNITY 2D fingerprints (Unity2D), and theoretical random selections (Random Jheo). Figure 13.6. Percent biological classes covered from the IC93 database versus subset sizes for maximum dissimilarity selections using selected MACCS substructure keys counting up to 1,3.5 or 9 occurrences of a particular fragment key, UNITY 2D fingerprints (Unity2D), and theoretical random selections (Random Jheo).
Table A.4 shows commonly used fragment keys the MACCS publicl66-keys. This table is used with the publicl66keys function above to produce a bit string key for use in filtering before substructure searching and for similarity computations. The table consists of SMARTS patterns3 used to identify each of 166 substructures. Table A.4 shows commonly used fragment keys the MACCS publicl66-keys. This table is used with the publicl66keys function above to produce a bit string key for use in filtering before substructure searching and for similarity computations. The table consists of SMARTS patterns3 used to identify each of 166 substructures.
Hicks MG, Jochum C. Substructure search systems. 1. Performance comparison of the MACCS, DARC, HTSS, CAS Registry, MYSSS, and S4 substructure search systems. J Chem Inf Comput Sci 1990 30 191-199. [Pg.510]

The fingerprint methods can be divided into dictionary-based and hashed-based methods. In the dictionary-based methods, such as the MDL MACCS keys [12] and BCI fingerprints [13], a binary fingerprint is defined in which each bit represents a particular substructural fragment contained in a fragment dictionary. The fingerprint... [Pg.619]

Anderson, S. Graphical Representation of Molecules and Substructure Search Queries in MACCS. J. Mol. Graphics 1984, 2, 83-90. [Pg.39]

As the biology database becomes more extensive and more historical, a greater need for more complex structure-activity searches will arise. These would allow a scientist to enquire for compounds that have specific structure and biological activity hmits. This draws on both MACCS and ORACLE simultaneously, and involves correlating the data retrieval from two different databases.Basically questions such as What compounds with this substructure have activity greater than X in result Y of test Z would be asked. These questions will require intersects and joins of fists of hits from ORACLE and MACCS. This has now been implemented with the fink . Future work involves optimisation and refinement of the report formats to handle radically different biological experimental designs better. [Pg.86]

The CDD staff concluded that MACCS could not rephcate the registration process of CSIS. It also did not provide the sophisticated generic substructure searching capabilities which are used in a least 45% of the searches executed by the CDD staff. The absence of an interface with the department s ancillary databases was another serious deficiency of MACCS. [Pg.94]

The Chemical Database operation controls storage, retrieval, searching, and deletion of data in MACCS databases. The chemistry treated by MACCS database operations, such as substructure searching, is included in this facility. [Pg.102]

SEARCH. With the current molecule from DRAW or ATTACH as a query, this menu provides for novelty search or substructure search operations over a MACCS database (Figure 2). The search algorithms treat stereochemistry, parent status, tautomerism, isomerism, formula, etc. This menu relies on the Chemical Database facility of MACCS-II among others. [Pg.102]

The structures of the Beilstein compounds are stored in connection tables (CT s) to allow a very flexible structure and substructure search. Since most commercially available structure/substructure handling programs such as MACCS (MDL) or DARC (Telesystemes/Questel) work on the basis of CT s, the Beilstein Registry Connection Table (BRCT) can be easily adapted for in-house systems. [Pg.191]


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MACCS

Substructural

Substructure

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