Property space

The increasing ranges of pressure and temperature of interest to technology for an ever-increasing number of substances would necessitate additional tables in this subsection as well as in the subsec tion Thermodynamic Properties. Space restrictions preclude this. Hence, in the present revision, an attempt was made to update the fluid-compressibihty tables for selected fluids and to omit tables for other fluids. The reader is thus referred to the fourth edition for tables on miscellaneous gases at 0°C, acetylene, ammonia, ethane, ethylene, hydrogen-nitrogen mixtures, and methyl chloride. The reader is also... 

The challenge in the synthesis of chemical libraries is the vast number of different, potentially drug-like small molecules which is estimated to be as high as 1060. As all of these molecules can never be synthesized and tested, it is essential to define criteria for the composition of libraries spanning the biologically relevant areas of the chemical space most efficiently. An important criterion of a compound library is its chemical diversity, a term describing the similarity or dissimilarity of all library components. Thus, chemical diversity expresses how well a library represents all theoretical possibilities within the chemical property space. A library with low... 

Molecular Properties and their Adaptability The Property Space of Molecular Entities... 

The concept of property space, which was coined to quanhtahvely describe the phenomena in social sciences [11, 12], has found many appUcahons in computational chemistry to characterize chemical space, i.e. the range in structure and properhes covered by a large collechon of different compounds [13]. The usual methods to approach a quantitahve descriphon of chemical space is first to calculate a number of molecular descriptors for each compound and then to use multivariate analyses such as principal component analysis (PCA) to build a multidimensional hyperspace where each compound is characterized by a single set of coordinates. 

The growing computahonal power available to researchers proves an invaluable tool to investigate the dynamic profile of molecules. Molecular dynamics (MD) and Monte Carlo (MC) simulahons have thus become pivotal techniques to explore the dynamic dimension of physicochemical properhes [1]. Furthermore, the powerful computational methods based in parhcular on MIFs [7-10] allow some physicochemical properhes to be computed for each conformer (e.g. virtual log P), suggesting that to the conformahonal space there must correspond a property space covering the ensemble of all possible conformer-dependent property values. 

The concept of property space is progressively being used to gain a deeper understanding of the dynamic behavior of a single compound in different media (as we illustrate below with acetylcholine, see Section 1.4.2) or bound to biological targets (the carnosine-carnosinase complex, see Section 1.4.3), but it can be used also with a set of compounds to derive fertile descriptors for dynamic QSAR analyses (4D QSAR, see Section 1.4.4). 

A property space can be defined using two classes of descriptors. The first class includes descriptors quantifying the variability (spread) of values their range is probably the most intuitive one. The second class of descriptors relates the dynamic behavior of a given property with other geometric or physicochemical properties. Such correlations can reveal if and how two molecular properties change in a coherent manner. 

Our first exploration of property space was focused on acetylcholine. This molecule was chosen for its interesting structure, major biological role, and the abundant data available on its conformational properties [15]. The behavior of acetylcholine was analyzed by MD simulations in vacuum, in isotropic media (water and chloroform) [16] and in an anisotropic medium, i.e. a membrane model [17]. Hydrated n-octanol (Imol water/4mol octanol) was also used to represent a medium structurally intermediate between a membrane and the isotropic solvents [17]. 

Notwithstanding this. Table 1.2 clearly shows that the behavior of acetylchoHne reflected the physicochemical properties of the simulated media by adapting its property space. This is particularly evident when examining the Hpophilicity averages, since the polarity of acetylcholine increased in aU media compared to vacuum although the differences between the mean log P values were small, they were significant as assessed by their 99.9% confidence Hmits. 

The second example of property space applications concerns the dipephde camo-sine (P-alanine-L-histidine, see Fig. 1.4) which represents the archetype of a series of histidine-containing dipeptides whose full physiological role remains poorly understood despite extensive studies in recent years [18-20]. Carnosine is synthesized by carnosine synthetase and hydrolyzed by dipeptidases (also called camosi-nases) which belong to the metalloproteases [21]. 

From a mathematical point of view, such correlahons may be analyzed by considering their regression coefficients. However, using regression coefficients as independent variables may lead to mathemahcal dead-ends. We thus looked for a descriptor of property space that would be both informative and simple to use. 

From a methodological viewpoint, our results suggest that range and sensitivity are useful descriptors of property spaces and can parameterize the capacity of a given molecule to span broad conformational and property spaces. In other words, range and sensitivity appear as promising descriptors of the dynamic behavior of a molecule. Their application to other dynamic QSAR studies [in particular, absorphon, distribution, metabolism and excretion (ADME) behavior] is under investigahon. 

Merschrod, K. A property-space perspective for interaction terms of ordinal variables. Quality and Quantity, 1982, 16, 549-558... 

B. Solvent constraints on the property space of acetylcholine. I. Isotropic solvents. f Med. Chem. 2005, 48, 1759-1767. 

Vistoli, G., Pedretti, A., Villa, L., Testa, B. Range and sensitivity as descriptors of molecular property spaces in dynamic QSAR analyses. /. Med. Chem. 2005, 48, 4947-4952. 

To further analyze the relationships within descriptor space we performed a principle component analysis of the whole data matrix. Descriptors have been normalized before the analysis to have a mean of 0 and standard deviation of 1. The first two principal components explain 78% of variance within the data. The resultant loadings, which characterize contributions of the original descriptors to these principal components, are shown on Fig. 5.8. On the plot we can see that PSA, Hhed and Uhba are indeed closely grouped together. Calculated octanol-water partition coefficient CLOGP is located in the opposite corner of the property space. This analysis also demonstrates that CLOGP and PSA are the two parameters with... 

Ultimately, when low LE series were chosen for further follow-up and medicinal chemistry optimization, modifications to the structure to produce more potent analogs would usually involve the addition of mass. This would move compounds to an undesired property space, and make the balancing of potency and PK... 

In addition to looking for data trends in physical property space using PCA and PLS, trends in chemical structure space can be delineated by viewing nonlinear maps (NLM) of two-dimensional structure descriptors such as Unity Fingerprints or topological atom pairs using tools such as Benchware DataMiner [42]. Two-dimensional NLM plots provide an overview of chemical structure space and biological activity/molecular properties are mapped in a 3rd and/or 4th dimension to look for trends in the dataset. 

The molecular descriptors refer to the molecular size and shape, to the size and shape of hydrophilic and hydrophobic regions, and to the balance between them. Hydrogen bonding, amphiphilic moments, critical packing parameters are other useful descriptors. The VolSurf descriptors have been presented and explained in detail elsewhere [8]. The VolSurf descriptors encode physico-chemical properties and, therefore, allow both for a design in the physico-chemical property space in order to rationally modulate pharmacokinetic properties, and for establishing quantitative structure-property relationships (QSPR). 

As such, VolSurf affords much structural information of use in designing compounds with optimal permeability profile, and in defining an ideal property space in similarity searches. 

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