MACCS key

Clearly, within the conceptual framework described above, there is extensive room for exploration in creating fingerprints and similarity measures to retrieve molecules based on varying conceptions of similarity [42—441. The simplest types of fingerprint consist simply of features indices that map the presence or absence of a small library of functional groups. The most well known and effective are the MACCS keys. These were initially chemical feature indices, that we later used successfully as a similarity metric. 

As illustrated in the next section, the use of biological fingerprints, such as from a BioPrint profile, provides a way to characterize, differentiate and cluster compounds that is more relevant in terms ofthe biological activity of the compounds. The data also show that different in silico descriptors based on the chemical structure can produce quite different results. Thus, the selection of the in silico descriptor to be used, which can range from structural fragments (e.g. MACCS keys), through structural motifs (Daylight keys) to pharmacophore/shape keys (based on both the 2D structure via connectivity and from actual 3D conformations), is very important and some form of validation for the problem at hand should be performed. 

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. 

The ISIS public keys are also known as MACCS keys. ISIS/Base and MACCS are both products of MDL Information Systems, Inc., San Leandro, CA, http // www.mdl.com/ (2003). 

MACCS keys substructure descriptors (0 structural keys)... 

Examples of structural keys are —> Augmented Atoms (AA), atom pairs and related descriptors, and —> atom-type Estate counts. However, the most common structural keys implemented in specific automated tools are MACCS keys, BCI keys, and CACTVS screen vectors [Ihlenfeldt, Takahashi et al, 1994 Voigt, Bienfait et al, 2001]. 

Two different MACCS keys (or MDL keys) [MACCS keys - MDL Information Systems Inc., 2008 Durant, Leland et al, 2002] are commonly encountered, one containing 960 bits and the other, which is public, containing a subset of 166 bits (ISIS keys). The fragment dictionary is based on a number of atom types, atom pairs, and custom atom environments. There can be a one-to-one relationship between the structural features and bits, or hashing can be used to create a many-to-one or many-to-many relationship between the features and bits. 

MACCS keys, MDL Information Systems, Inc., 14600 Catalina Street, San Leandro, CA. 

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... 

The number of features combined in a vector-type representation is indicative of the dimensionality of the problem space. Low-dimensional representations, on the one hand, allow easy visualization but are most often not very discriminative. Highdimensional representations, on the other hand, such as those encoded in Daylight fingerprints [23], MACCS keys [24], or UNITY fingerprints [25], provide more detailed accounts on structural or chemical variations. However, this is achieved at the cost of visualization. Part of these high-dimensional representations describe specific local features of molecules, and because not all molecules in the data contain these features, gaps or zeros are introduced in the data representation. For certain data mining methods, this could be problematic. In many cases, dimensionality reduction procedures are applied to reduce the complexity of the representation. The reduction of the dimensionality is accomplished by means of 1) variable selection procedures, 2)... 

Compound ICsoiSE (fiM) hPRMTl Tanimoto coefficient MACCS keys Tanimoto coefficient graph-3-point pharmacophore Actual docking rank COLD... 

The Tanimoto similarity indices are calculated on the basis of MACCS keys and graph-3-point pharmacophore fingerprints. The actual docking rank from the docking of the 6236 Chembridge compounds is indicated. 

Compound Ki (fiM) Tanimoto coefficient graph-3-point pharmacophore Tanimoto coefficient MACCS keys Docking rank COLD/ ColdScore... 

Seven 2D MOE descriptors and 51 fragment count descriptors (subset of the 166-bit MACCS keys) were calculated. 

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