Property landscapes molecular similarity

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... 

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. 

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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