Activity landscape

Fig. 13.1 An ideal activity landscape. In such landscapes, which are conceptually similar to the gently rolling Flint Hills of Kansas depicted in the figure, it is generally assumed that similar molecules have similar biological activities [3]. It is clear from the figure that even relatively sparse sampling of chemistry space may be sufficient to construct a reasonable estimate of the activity landscape.

Med. Chem. 2002, 45, 4350-4358. Shanmugasundaeam, V. and Maggioea, G.M. Characterizing property and activity landscapes using an information-theoretic approach. Abstr. Papers ACS, 32-CINF, Part 1 August 2001, 222. 

Fig. 2. Example of rough activity landscape. This figure shows the activity landscape for a series of related antibacterial compounds plotted in using the 2D BCUT descriptors to arrange the compounds. (A) Shows how the compounds are arrayed in a 2D representation of the chemistry space with the height of the marker being proportional to the minimum inhibitor concentration of the compounds [the smaller the minimum inhibitory concentration (MIC) the more potent the compound]. (B) This second panel presents the upper figure as a 2D figure to enhance the sharp cutoff between active and inactive compounds, emphasizing the point that activity landscapes are rarely smooth continuous functions.

Guha, R., Lajiness, M. S., van Drie, J. H. (2009) Navigating structure activity landscapes. Drug Discovery Today 14, 698-705. 

Key words Structure-activity relationship, QSAR, Inverse QSAR, Structure-activity landscapes,... 

Activity cliff, Structure-activity similarity maps, Structure-activity landscape index, Structure-activity... 

Structure-activity similarity (SAS) maps, first described by Shanmugasundaram and Maggiora (35), are pairwise plots of the structure similarity against the activity similarity. The resultant plot can be divided into four quadrants, allowing one to identify molecules characteristic of one of four possible behaviors smooth regions of the SAR space (rough), activity cliffs, nondescript (i.e., low structural similarity and low activity similarity), and scaffold hops (low structural similarity but high activity similarity). Recently, SAS maps have been extended to take into account multiple descriptor representations (two and three dimensions) (36, 37). In addition to SAS maps, other pairwise metrics to characterize and visualize SAR landscapes have been developed such as the structure-activity landscape index (SALI) (38) and the structure-activity index (SARI) (39). 

Medina-Franco JL, Martinez-Mayorga K, Bender A et al (2009) Characterization of activity landscapes using 2D and 3D similarity methods consensus activity cliffs. J Chem Inf Model 49(2) 477-491... 

Yongye AB, Byler K, Santos R et al (2011) Consensus models of activity landscapes with multiple chemical, conformer, and property representations. J Chem Inf Model 51(6) 1259-1270... 

Guha R, Van Drie JH (2008) The structure-activity landscape index identifying and quantifying activity-cliffs. J Chem Inf Model 48(3) 646-658... 

Figure 11. Amines B that were resorted according to the Tanimoto similarity and used for running the GA on a resorted structure-activity landscape.

Figure 4.2 Schematic illustration of an activity landscape. A two-dimensional chemical reference space is defined by two molecular descriptors. Biological compound activity adds a dimension to the reference space, forming the surface of an activity landscape.

In systematic SAR analysis, molecular structure and similarity need to be represented and related to each other in a measurable form. Just like any molecular similarity approach, SAR analysis critically depends on molecular representations and the way similarity is measured. The nature of the chemical space representation determines the positions of the molecules in space and thus ultimately the shape of the activity landscape. Hence, SARs may differ considerably when changing chemical space and molecular representations. In this context, it becomes clear that one must discriminate between SAR features that reflect the fundamental nature of the underlying molecular structures as opposed to SAR features that are merely an artifact of the chosen chemical space representation. Consequently, activity cliffs can be viewed as either fundamental or descriptor- and metrics-dependent. The latter occur as a consequence of an inappropriate molecular representation or similarity metrics and can be smoothed out by choosing a more suitable representation, e.g., by considering activity-relevant physicochemical properties. By contrast, activity cliffs fundamental to the underlying SARs cannot be circumvented by changing the reference space. In this situation, molecules that should be recognized as... 

SAR Index. The SAR Index (SARI) has recently been introduced to quantitatively capture the continuous, discontinuous, or heterogeneous nature of activity landscapes and SARs. Similar to SAS maps, it exclusively relies on the 2D structural similarity and potency distribution within a set of active compounds. However, SARI aims to categorize the SARs of a population of compound sets without employing idealized reference states. It generates a numerical index between 0 and 1 that reflects the (dis-)continuity of the SARs under consideration. SARI distinguishes between three major categories of... 

The discontinuity score assesses the discontinuous features of an activity landscape. 

Structure-activity relationships have been largely employed for molecular design these correlations depend on the molecular representation and the activity landscape. The molecular representation depends only on the small molecule, whereas the activity landscape provides information on the ligand-receptor complex, for example, how permissive the binding pocket is. To exemplify the molecular similarity approach, a set of odorants (compared to benzaldehyde) will be presented. 

Peltason, L. and Bajorath, J. (2007). Molecular similarity analysis uncovers heterogeneous structure-activity relationships and variable activity landscapes. Chem. Biol. 14, 489 97. Peltason, L. and Bajorath, J. (2008). Molecular similarity analysis in virtual screening. In "Chemoinformatics Approaches to Virtual Screening", (A. Vamek and A. Tropsha, eds), pp. 120-147. RSC Publishing, Cambridge, UK. 

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