Descriptor Daylight

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 order to calculate a physicochemical property, the structure of a molecule must be entered in some manner into an algorithm. Chemical structure notations for input of molecules into calculation software are described in Chapter 2, Section VII and may be considered as either being a 2D string, a 2D representation of the structure, or (very occasionally) a 3D representation of the structure. Of this variety of methods, the simplicity and elegance of the 2D linear molecular representation known as the Simplified Molecular Line Entry System (SMILES) stands out. Many of the packages that calculate physicochemical descriptors use the SMILES chemical notation system, or some variant of it, as the means of structure input. The use of SMILES is well described in Chapter 2, Section VII.B, and by Weininger (1988). There is also an excellent tutorial on the use of SMILES at www.daylight.com/dayhtml/smiles/smiles-intro.html. 

The first application using MDS in molecular diversity analysis was introduced by a group at Chiron as a means of reducing the enormous dimensionality of binary chemical descriptors They found that 2048-bit Daylight fingerprints associated with 721 commercially available primary amines could be reduced to only five dimensions that reproduced all 260,000 original dissimilarities with a standard deviation of only 10%. Similarly, only seven dimensions were required to reduce the 642,000 pairwise similarities among a set of 1133 carboxylic acids and acid chlorides to the same standard deviation. 

A molecular descriptor generator to encode the structural information of the compounds. We obtained the best results with 2-dimensional fingerprints based on topology of the molecules such as the 2,048-bit default Daylight or 1,024-bit folded Scitegic ECFP 4 descriptors (tee Note 2). 

In a later study, three-point and four-point pharmacophore fingerprints have been shown to perform better than 2D Daylight fingerprints in some circumstances [48], More recently, the mini-fingerprints (MFPs), based on a small number of 2D descriptors, were specifically designed for similarity searching and were shown to have comparable performance to the 3D pharmacophores [49]. 

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