Substructure search keys

A handful of tables that contain database parameters. These include substructure search key definitions, the periodic table used with the database, and a list of salt moieties that can be considered during searches. 

Binary Data. Data stored in a file or database that is not chemist-readable, and usually cannot be converted to printable characters. Examples include connection table storage in a database, substructure search keys, and a graphics image of a structure. Note that some other data that is also not chemist-readable, like certain linear notations (e.g., a Chime string), may be made up of printable characters and is not strictly binary data. 

Inverted Keys. When substructure search keys are generated for a structure, they may be stored in normal order (where each record represents a structure, and the bits or fields for that structure represent the keys). Alternatively, they may be stored in inverted or pivoted order, where each record represents a given substructure key, and the bits represent structures that have that particular key set. This type of storage benefits key searching, where a user wants all the structures that have a particular key set. 

Development of Search Keys. An analysis was made of previous DTP searches (16), and keys were developed that would make effective use of the detailed Information content of the CAS Registry III connection table. Although some thought was given to developing novel substructure search keys, the reality of our development schedule led to the use of a combination of previously developed key types that have been found efficient. Figure 4 shows a mythical example of an abbreviated DTP structure record and sample of each type of search key. A description of each follows. 

Key words Protein kinase, kinase-targeted library, library design, kinase chemical cores, substructure search, SMARTS Query, subsetting, binding mode annotation. 

CDD has developed and deployed a robust preliminary public antimalarial database from five sources which hosts data on approximately 16,000 public compounds. The growth of this database has fostered several key antimalarial discovery collaborations between CDD users. A substructure search for the known chemo-sensitizer substructure led to the identification of hundreds of compounds for laboratory evaluation by the laboratories of... 

Substructure searching is often used in drug design and needs no further clarification. Similarity searching is also a very well known technique described in more detail elsewhere [52], We usually use M ACCS keys, Unity fingerprints, CATS descriptors, and feature trees for similarity searching [53], Each technique has its own strengths and weaknesses, so we favor parallel application of two or three of them. 

Another crucial aspect of the validation process is the test of how well described and represented the molecule is in the map of the chemical toxicity space that the regression equation represents. If the substructural key does not exist in the database used to build the model, then it is unlikely that the compound can be accurately estimated. In addition, if compounds similar to the test compound do not exist, then a comparison as was done above cannot be conducted and a measure of the performance of the model with compounds similar to the test material cannot be made. This type of validation requires a large database and a substructural search algorithm, and should be included in a QSAR estimate. 

The BASIC has for some time been performing substructure search on the entire CAS file. Thus, BASIC keys are routinely generated in large volume for compounds newly registered at CAS and can be used for surveilance at low cost. 

Other common descriptors derived from substructure-based methods are discussed below. Among these, hash structural codes, structural keys, and fingerprints are mostly applied in virtual screening and substructure searching, whereas pharmacophore-based descriptors are more successful in similarity/diversity analysis and QSAR/QSPR studies. 

In order to find the correct rate estimation parameters for a bimolecular reaction, one must first find the best-match functional group A in one reactant, using a substructure search algorithm very similar to that used to find thermochemical group values. Then one must find the best-match functional group B in the second reactant in a similar way. Then one can look up the rate estimation parameters for this reaction type using A and B as the keys. 

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-... 

This is the fragment key for phenol. The key function can be used to compute and store values of the fragment key in tables of molecular structures. It can also be used to compute values of fragment keys for substructures to be used as a prescreen during a full substructure search using the matches function. 

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.

Several product-based approaches to library design that do not require full enumeration have been developed. Pickett et al. have described the design of a diverse amide library where diversity is measured in product space. The DIVSEL program is a DBCS method where dissimilarity is measured in three-point pharmacophore space [83]. Initially, 11 amines were selected based on maximum pharmacophore diversity. Then a total of 1100 carboxylic acids were identified following substructure searching. A set of 1100 pharmacophores keys was generated, where each key corresponds to one acid combined with the 11 amines. DIVSEL was used to select 100 acids based on the diversity of the products. The final library was found to cover 85% of the pharmacophores represented by the entire 12,100 virtual libraries. 

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