Property landscapes similarity

The identification and optimisation of drug-like small molecule inhibitors for kinase targets is a highly competitive arena and given the high level of structural similarity across family members it is unsurprising that the intellectual property landscape has become crowded, especially around some of the more validated targets. The situation is particularly apparent for type I kinase... 

SAS maps have also been used to represent consensus models of activity landscapes by means of fusing similarity measures obtained from different 2D and 3D molecule representations and similarity functions [145,189,191]. In addition, SAS maps were recently adapted to model multitarget activity landscapes by representing in one axis the activity similarity of compound datasets screened across multiple biological endpoints [192]. SAS maps have been extended to characterize property landscapes other than activity landscapes. For example, stmcture-flavor similarity maps have been proposed to systematically characterize stmcture-flavor associations of a comprehensive flavor database [182]. A number of additional types of 2D MFS maps that characterize a different fusion-based similarity on each axis have also been developed and are described in Section 15.5.3. 

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. 

Based on the descriptions of spatial variation in each environmental compartment, multimedia models can be categorized into multimedia compartmental models (MCMs) [3-20], spatial multimedia models (SMs) [21-24] and spatial multimedia compartmental models (SMCMs) [25-27]. MCMs assume homogeneous landscape properties in each medium and assume all environmental compartments are well mixed. SMs are collections of single-media models in which the output of one model serves as the input to the others. Each individual model in the SMs is a spatial model describing the variation of environmental properties in one or more directions. SMCMs are similar to MCMs, but consider one or more environmental compartments as nonuniform regions. 

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. 

One of the most important landscape properties is autocorrelation, a measure of similarity of fitnesses of neighboring points. Uncorrelated landscapes may have very dissimilar fitness values for adjacent points and are called rugged. Formally, for landscapes which are stationary (have the same mean, variance and autocorrelation throughout the space), autocorrelation is defined as... 

The properties of p-spin landscapes have been studied in detail [34,79], One observation of importance is that p plays a role similar to that of K. The number of local peaks increases exponentially with p [76], This occurs because, as p increases, there are more conflicting constraints that cause the site values that maximize one J function to conflict with those site values that maximize other J functions. 

The input required by multimedia fate models includes properties of the chemicals (such as distribution over compartments air, water, and soil or sediment), properties of the environment or landscape receiving the contaminants, and emission patterns and mode of entry of chemicals into the environment (OECD 2004) (Figure 1.1). Fenner et al. (2005) compared the outcome of 9 multimedia fate models by applying them to a set of 3175 hypothetical chemicals covering a range of 25 half-life combinations (in water, air, or soil or sediment) and 127 combinations of partition coefficients (air-water (/<", ), Kov/, and Koa). Results show great similarities between the model outputs for Pov predictions, but less for LRTR Pov and, to a lesser extent,... 

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... 

Crystallization usually involves very long time scales (at least when compared to time scales of routine calculations) and complicated potential energy landscapes. Computer simulations of this process are, therefore, considered to be difficult in general. In a series of papers, Haymet and coworkers investigated the structure and dynamics of the ice/water interface. In their approach, the pre-built patches of water and ice were put together to create the interface. The necessity to simulate the highly improbable creation of the crystallization nucleus was thus avoided. Similar setup was used by other groups to assess various properties of the ice/water interface. ... 

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