MACCS fingerprints

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. 

In addition to the commonly used MACCS structural fragment keys (MDL Information Systems, Inc., San Leandro CA) and Daylight or UNITY fingerprints (Daylight... 

Several commercial providers of chemoinformatic tools have issued systems for calculating molecular fingerprints. Commonly used formats are those used in the molecular structure database systems developed by MDL (ISIS and MACCS) and the Daylight fingerprints. 

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

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).

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)... 

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. 

Fig. 2.12 Molecular Design Limited Molecular ACCess System (MACCS) fingerprints computed for Ames data set...

An exhaustive analysis of 2995 molecule pairs extracted from the 98.1 version of Bioster database indicated that similarity measures based on 2D molecular fingerprints or electrostatic field descriptors were complementary although 2D methods could be adequate for similarity analyses [55]. To evaluate a range of similarity measures among synthetic substances and natural products, the Willett group also used 5024 compounds from Bioster database as well as sets of selected bioactive compounds from the more populous Chemical Abstract Service, ID-Alert, MACCS Drug Data Report, and NCI AIDS databases [56]. 

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