Physicochemical prediction

Structure-based tools such as applications databases and physicochemical prediction will not replace chromatographers, but rather allow them to use their knowledge of chromatography, relating it to the analyte functionality and making excellent choices of initial conditions that reap faster, better separations in less time. 

Many of the physicochemical properties of interest are dependent on the solid form and, unfortunately, successful prediction of polymorphic forms is inexact. This, in combination with the fact that prediction of physicochemical properties is also very challenging, makes ah initio prediction very difficult and imprecise. However, some discussion of predictive tools is included in this chapter. A general comment regarding ah initio prediction is that "order of magnitude" predictions may be possible once some basic physicochemical information is available. However, the complexity and diversity of the chemistry space make reliable predictions across a broad spectrum of chemical structures very difficult. It is not surprising then that physicochemical predictions across more narrowly defined chemical spaces (e.g., chemical or therapeutic classes) can be more reliable and useful. Drug delivery, formulation, and computational chemistry experts will likely be able to provide a useful perspective on opportunities to take advantage of such ah initio predictions within the chemistry space that discovery teams often operate. 

The next question is how to represent the reacting bonds of the reaction center. We wanted to develop a method for reaction classification that can be used for knowledge extraction from reaction databases for the prediction of the products of a reaction. Thus, we could only use physicochemical values of the reactants, because these should tell us what products we obtain. 

The method of building predictive models in QSPR/QSAR can also be applied to the modeling of materials without a unique, clearly defined structure. Instead of the connection table, physicochemical data as well as spectra reflecting the compound s structure can be used as molecular descriptors for model building,... 

These few examples are of course a small and arbitrarily chosen set of methods for the calculation of log P values. Nevertheless, it is hoped that they demonstrate some basic principles in the prediction of a physicochemical property. 

An extensive series of studies for the prediction of aqueous solubility has been reported in the literature, as summarized by Lipinski et al. [15] and jorgensen and Duffy [16]. These methods can be categorized into three types 1 correlation of solubility with experimentally determined physicochemical properties such as melting point and molecular volume 2) estimation of solubility by group contribution methods and 3) correlation of solubility with descriptors derived from the molecular structure by computational methods. The third approach has been proven to be particularly successful for the prediction of solubility because it does not need experimental descriptors and can therefore be applied to collections of virtual compounds also. 

A proper representation of the molecular structure is crucial for the prediction of spectra. Fragment-based methods, topological descriptors, physicochemical descriptors, and 3D descriptors have been used for this endeavor. 

Prediction of various physicochemical properties such as solubihty, lipophhicity log P, pfQ, number of H-donor and acceptor atoms, number of rotatable bonds, polar surface area), drug-likeness, lead-likeness, and pharmacokinetic properties (ADMET profile). These properties can be applied as a filter in the prescreening step in virtual screening. 

It is also important to develop an understanding of the movement of chemicals through the environment by investigating their fate and behaviour. Based on a chemical s inherent physicochemical properties, it is possible to predict with some degree of certainty which environmental compartment it is likely to reside in and to what extent it is likely to be bioavailable and accumulate through the food chain. 

The influence of physicochemical factors is closely related to surface phenomena at the solid-liquid boundary. It is especially manifested by the presence of small particles in the suspension. Large particle sizes result in an increase in the relative influence of hydrodynamic factors, while smaller sizes contribute to a more dramatic influence from physicochemical factors. No reliable methods exist to predict when the influence of physicochemical factors may be neglected. However, as a general rule, for rough evaluations their influence may be assumed to be most pronounced in the particle size range of 15-20 tm. 

The applications of quantitative structure-reactivity analysis to cyclodextrin com-plexation and cyclodextrin catalysis, mostly from our laboratories, as well as the experimental and theoretical backgrounds of these approaches, are reviewed. These approaches enable us to separate several intermolecular interactions, acting simultaneously, from one another in terms of physicochemical parameters, to evaluate the extent to which each interaction contributes, and to predict thermodynamic stabilities and/or kinetic rate constants experimentally undetermined. Conclusions obtained are mostly consistent with those deduced from experimental measurements. 

While chemists differed on the relative importance of prediction and accommodation, it seems fair to approximate the consensus as follows. The reasons for accepting the periodic law are, in order of importance, [1] it accurately describes the correlation between physicochemical properties and atomic weights of nearly all known elements ... 

The physicochemical properties of carbonaceous materials can be altered in a predictable manner by different types of treatments. For example, heat treatment of soft carbons, depending on the temperature, leads to an increase in the crystallite parameters, La and Lc and a decrease in the d(0 0 2) spacing. Besides these physical changes in the carbon material, other properties such as the electrical conductivity and chemical reactivity are changed. A review of the electronic properties of graphite and other types of carbonaceous materials is presented by Spain [3],... 

This equation has been derived as a model amplitude equation in several contexts, from the flow of thin fluid films down an inclined plane to the development of instabilities on flame fronts and pattern formation in reaction-diffusion systems we will not discuss here the validity of the K-S as a model of the above physicochemical processes (see (5) and references therein). Extensive theoretical and numerical work on several versions of the K-S has been performed by many researchers (2). One of the main reasons is the rich patterns of dynamic behavior and transitions that this model exhibits even in one spatial dimension. This makes it a testing ground for methods and algorithms for the study and analysis of complex dynamics. Another reason is the recent theory of Inertial Manifolds, through which it can be shown that the K-S is strictly equivalent to a low dimensional dynamical system (a set of Ordinary Differentia Equations) (6). The dimension of this set of course varies as the parameter a varies. This implies that the various bifurcations of the solutions of the K-S as well as the chaotic dynamics associated with them can be predicted by low-dimensional sets of ODEs. It is interesting that the Inertial Manifold Theory provides an algorithmic approach for the construction of this set of ODEs. 

In recent decades, many investigations have been carried out on the solubilization and on the physicochemical characterization of a wide variety of substances confined in water-containing reversed micelles. Even if these studies have not produced a general theory to predict a priori all the effects accompanying the solubihzation process, some general aspects nonetheless have been underhned. In the following, the results of some of these investigations, selected to show the extent of some peculiar behaviors, will be reported. 

A Brief Review of the QSAR Technique. Most of the 2D QSAR methods employ graph theoretic indices to characterize molecular structures, which have been extensively studied by Radic, Kier, and Hall [see 23]. Although these structural indices represent different aspects of the molecular structures, their physicochemical meaning is unclear. The successful applications of these topological indices combined with MLR analysis have been summarized recently. Similarly, the ADAPT system employs topological indices as well as other structural parameters (e.g., steric and quantum mechanical parameters) coupled with MLR method for QSAR analysis [24]. It has been extensively applied to QSAR/QSPR studies in analytical chemistry, toxicity analysis, and other biological activity prediction. On the other hand, parameters derived from various experiments through chemometric methods have also been used in the study of peptide QSAR, where partial least-squares (PLS) analysis has been employed [25]. 

Purdy [91] used the technique to predict the carcinogenicity of organic chemicals in rodents, although his model was based on physicochemical and molecular orbital-based descriptors as well as on substructural features and it used only a relatively small number of compounds. His decision tree, which was manual rather than computer based, was trained on 306 compounds and tested on 301 different compounds it achieved 96% correct classification for the training set and 90% correct classification for the test set. 

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