Chemical spaces Fingerprinting

Methods of analyzing the diversity of the selected subset ensure that an appropriate chemical space is covered. Descriptors such as fingerprints, and 2D, and 3D descriptors, as well as molecular surface properties, which can be... 

The full matrix nature of the BioPrint database also enables an analysis of targets in drug chemical space. In this approach, a target is characterized by a fingerprint of the activities of a fixed set of compounds (the drug and reference compound set) against... 

Figure 2.18 Chemogenomic analysis of targets BioPrint targets clustered in chemical space by fingerprint of activities against the consistent BioPrint compound set.

Fig. 26 Kohonen maps of the diverse screening libraries. Gray levels indicate the population of a cell. Light gray cells contain only one compound black cells indicate the highest population for each map (a 141, b 12, c 8). The chemical space on the maps is defined by a) fingerprints, b) substmcture descriptors, and c) autocorrelation coefficients (from three-dimensional structure).

Briefly, DynaMAD is designed to map database compounds to activity-specific consensus positions in chemical space representations of stepwise increasing dimensionality [38] and ACCS-FP is utilized in conventional fingerprint search calculations using multiple reference compounds [61]. 

An alternative to fingerprint based similarities are those based on BCUTs (Burden, CAS, University of Texas). This method uses a modified connectivity matrix (the Burden matrix) onto which are mapped atomic descriptors (such as atomic mass and polarizability) and connectivity information. The eigenvectors of this matrix represent a compressed summary of the information in the matrix and are used to describe a molecule. Typically 5-6 BCUT descriptors suffice to describe the chemical space of a set of molecules, and the space is usually partitioned into distinct bins , with each molecule assigned to the appropriate partition. In this format, similarity calculations become very simple molecules which are mapped into the same partition are similar. As an alternative, one could use larger numbers of molecular properties and a correlation vector approach. 

A. Because each molecule is processed only once to produce the centroid fingerprint, the method is fast and applicable to very large databases. But although such methods scaled well (order N), the diversity measures were not well behaved. For example, the addition of a redundant molecule in chemical space (a molecule with the same similarity relationship to other molecules in the set as an existing molecule) could cause the diversity measure to either increase... 

However, the fingerprint driven diversity methods suffer from an inability to describe a bounded chemical space novel molecules can be added with a concurrent increase in diversity with little indication of how evenly sampled parts of the space is. In this context, partition based methods promise much. The BCUT descriptors are particularly suitable for the definition of a bounded chemical space and have the added bonus that they are quickly calculated. Partition based methods also scale very well in that it is only necessary to calculate which bin the molecule falls into, not to compute all pairwise similarities with the other molecules in the set. Absolute and relative diversity may be computed from bin occupancy, for example the number of bins covered by a compound set. 

FIGURE 15.7 3D projections of PCA-based chemical spaces generated from a set of 2250 compounds obtained from nine datasets of 250 compounds each using four different molecular fingerprints (Atom pairs, MACCS keys, TGD, and piDAPH4) and the Tanimoto similarity function (see text for further details). For color details, please see color plate section. 

As described before, it is due to the vast amount of possible structures that one can never get an adequate sample of chemical space. One question is if the entire chemical space is relevant for finding pharmacologically active compounds and how to predict this for future targets [19]. Another question is how to sample a part of chemical space in a uniform, systematic fashion. Often, the answer is considered to be a diverse selection. However, what is diversity [20] The usual method to describe diversity is to determine Tanimoto distances. These coefficients are calculated by comparing the number of shared and unique molecular fingerprints within a pair of structures. Usually, compounds with Tanimoto >0.85 are considered to be similar. The lower the Tanimoto coefficients in a compound set are, the more structurally diverse the set can be... 

Figure 15.6 "Chemical space" plots illustrating the chemical diversity of a screening library. In the chemical space plots, each point represents a compound and the proximity of two points is indicative of the structural similarity (as defined by two-dimensional fingerprints and a Tanimoto index [69]) between the corresponding compounds. In (a) 13 hit series, in which active compounds were identified, are highlighted. Three of these are circled, corresponding to series 8,11, and 13, which are analyzed in more detail in Figure 15.7. The...

---