Molecular similarity activity landscapes

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... 

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. 

Peltason, L. and Bajorath, J. (2007) Molecular similarity analysis uncovers heterogeneous structure-activity relationships and variable activity landscapes. Chemistry e[ Biology, 14 (5), 489-497. 

The systematic exploration of activity cliffs from various representations of activity landscapes is summarized in an interesting article by Stumpfe and Bajorath [79]. These representations could include either distinct chemical transformations (MMPs) or similarity-based relationships between molecules. In a classical medicinal chemistry approach, activity cliffs are extracted from R-group tables, as shown in Figure 10.3 for 3-oxybenzamides as factor Xa inhibitors [58, 59]. This has to be repeated for each scaffold of interest with a predefined view on informative attachment points at the molecular core. Hence this approach is feasible for smaller datasets with only a few informative attachment points for SAR investigation, typically a narrow series in lead optimization. 

Section 15.4 provides a discussion of similarity measures, which depend on three factors (1) the representation used to encode the desired molecular and chemical information, (2) whether and how much information is weighted, and (3) the similarity function (sometimes called the similarity coefficient) that maps the set of ordered pairs of representations onto the unit interval of the real line. Each of these factors is discussed in separate subsections. Section 15.5 presents a discussion of a number of questions that address significant issues associated with MSA Does asymmetric similarity have a role to play Do two-dimensional (2D) similarity methods perform better than three-dimensional (3D) methods Do data fusion and consensus similarity methods exhibit improved results Are different similarity measures statistically independent How do we compare similarity methods Can similarity measures be validated S ection 15.6 provides a discussion of activity landscapes... 

Since it is highly unlikely that a single representation and set of descriptors will capture aU of the many different aspects of molecular and chemical information [57] needed for specific MSAs, the use of multiple representations have been proposed in different applications such as similarity searching [58,59], diversity analysis [25-27,60], and activity landscape modeling (see Sections 15.6.2 and 15.6.3). 

An issue that comes into both similarity fnsion and consensus methods is the independence of the different similarity measnres employed in a given procedure. This issue also occurs in activity landscape modeling (see Section 15.6.1) when fnsion or related methods are used to estimate molecular similarities [144, 146]. In snch cases, the qnestion typically arises when two methods produce highly correlated values as to whether the set of values from one of them should be removed from the fusion process. The answer seems to be yes, althongh most of the time in cheminfor-matics it is ignored. But should it be ... 

AN APPLICATION OF MOLECULAR SIMILARITY ANALYSIS TO CHEMICAL SPACE AND ACTIVITY LANDSCAPES... 

While the chemical universe of molecitles potentially relevant in food science is considerably smaller, it nonetheless is large enough to benefit from many of the chemical informatic concepts that have proved useful in medicinal chemistry and related fields of chemistry. Two of these concepts, molecttlar similarity and chemical space (CS), are dealt with in this chapter. Of the two, molecular similarity is more fundamental since it plays a cmcial role in the definition of CS itself. Though important, activity or property landscapes, which provide the third leg of a triad of activities that play important roles in much of chemical informatics, will not be discussed here. Numerous recent publications describing the visual and statistical aspects of activity landscapes as well as the basic features of these landscapes should be consrrlted for details [4-8],... 

Over the past two decades, computational methods have been playing an ever-in-creasing role in drag discovery research due especially to the burgeoning amount of data being generated by ever faster and more powerful experimental techniques. Three concepts, molecular similarity, CS, and activity/property landscapes, in some fashion underlie all of these methods— the current woik addresses molecular/strac-tural similarity and CS, two important pillars supporting the edifice of chemical informatics. 

Iyer P, Stumpfe D, Vogt M, Bajorath J, Maggiora GM (2013) Activity landscapes, information theory, and structure-activity relationships. Mol Inf32 421-430 Vogt M, Iyer P, Maggiora GM, Bajorath J (2013) Conditional probabilities of activity landscape features for individual compounds. J Chem Inf Model 53 1602-1612 Rouvray DH (1990) The evolution of the concept of molecular similarity. In Johnson MA, Maggiora GM (eds) Concepts and applications of molecular similarity, chapter 2. Wiley, New York... 

In applied molecular evolution, fitness generally has one of two meanings (i) It can refer specifically to how well a molecule performs a desired function, typically the affinity of a ligand for a given receptor or its catalytic activity for a given reaction, (ii) It can refer to the rate at which a molecule in a population of molecules is copied over one iteration, similar to the notion of enrichment in die molecular diversity literature. This second definition is more complex, as fitness depends not only on the properties of a molecule but also on the properties of the rest of the population. Since fitness then changes each iteration as the population changes, the whole fitness landscape metaphor is weakened. For these reasons, I will restrict myself to the first definition of fitness. 

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