Property estimation from molecular structures

One solution to this quagmire has been the use of calculated properties estimated from the molecular structure of chemicals instead of their experimental data. Molecular descriptors calculated using different variations of the chemical stmcture lead to the development of quantitative structure-property/activity relationship (QSPR/QSAR) models. 

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. 

There are a number of properties of molecules that are additive to a reasonable approximation, i.e. the value of such a property of a given molecule is an approximate sum of the values of the properties of either the atoms or bonds present. It has been shown that the dielectric constant is related to some additive properties and it is thus possible to make some estimate of dielectric properties from consideration of molecular structure. 

The US EPA T.E.S.T. is a downloadable program to estimate different toxicological endpoints and physicochemical properties from molecular structure using a variety of QSAR methodologies [58],... 

Pollutants with high VP tend to concentrate more in the vapor phase as compared to soil or water. Therefore, VP is a key physicochemical property essential for the assessment of chemical distribution in the environment. This property is also used in the design of various chemical engineering processes [49]. Additionally, VP can be used for the estimation of other important physicochemical properties. For example, one can calculate Henry s law constant, soil sorption coefficient, and partition coefficient from VP and aqueous solubility. We were therefore interested to model this important physicochemical property using quantitative structure-property relationships (QSPRs) based on calculated molecular descriptors [27]. 

Normal-phase liquid chromatography is thus a steric-selective separation method. The molecular properties of steric isomers are not easily obtained and the molecular properties of optical isomers estimated by computational chemical calculation are the same. Therefore, the development of prediction methods for retention times in normal-phase liquid chromatography is difficult compared with reversed-phase liquid chromatography, where the hydrophobicity of the molecule is the predominant determinant of retention differences. When the molecular structure is known, the separation conditions in normal-phase LC can be estimated from Table 1.1, and from the solvent selectivity. A small-scale thin-layer liquid chromatographic separation is often a good tool to find a suitable eluent. When a silica gel column is used, the formation of a monolayer of water on the surface of the silica gel is an important technique. A water-saturated very non-polar solvent should be used as the base solvent, such as water-saturated w-hexane or isooctane. 

Modern many-body methods have become sufficiently refined that the major source of error in most ab initio calculations of molecular properties is today associated with truncation of one-particle basis sets e.g. [1]- [4]) that is, with the accuracy with which the algebraic approximation is implemented. The importance of generating systematic sequences of basis sets capable of controlling basis set truncation error has been emphasized repeatedly in the literature (see [4] and references therein). The study of the convergence of atomic and molecular structure calculations with respect to basis set extension is highly desirable. It allows examination of the convergence of calculations with respect to basis set size and the estimation of the results that would be obtained from complete basis set calculations. 

Physical property estimation techniqnes fall into two major categories, equation based and stractnre based. Equation-based techniqnes determine the valne of an unknown physical property from the valnes of one or more known physical properties. Structure-based techniques determine the valne of an nnknown physical property from a function of molecular structure. 

To a chemist concerned Tyith the synthesis of new high-explosive compounds the ability to compute detonation properties (detonation pressure, energy, and velocity as well as product composition) from a given molecular structure and the known or estimated crystal density is a problem of the utmost importance. The calculated properties could be meaningful in the decision as to whether it is worth the effort to attempt a new and complex synthesis. One reason behind the recent development of detonation-properties programs for use on high-speed computers has been to supply this desired information. One such program, the ruby code,1 has recently been made available to a number of laboratories, the authors included. 

Therefore, similar to the attempts made to estimate vapor pressure (Section 4.4) there have been a series of quite promising approaches to derive topological, geometric, and electronic molecular descriptors for prediction of aqueous activity coefficients from chemical structure (e.g., Mitchell and Jurs, 1998 Huibers and Katritzky, 1998). The advantage of such quantitative structure property relationships (QSPRs) is, of course, that they can be applied to any compound for which the structure is known. The disadvantages are that these methods require sophisticated computer software, and that they are not very transparent for the user. Furthermore, at the present stage, it remains to be seen how good the actual predictive capabilities of these QSPRs are. 

Optical properties also provide useful structure information about the fiber. The orientation of the molecular chains of a fiber can be estimated from differences in the refractive indexes measured with the optical microscope, using light polarized in the parallel and perpendicular directions relative to the fiber axis (46,47). The difference of the principal refractive indexes is called the birefringence, which is illustrated with typical fiber examples as follows. Birefringence is used to monitor the orientation of nylon filament in melt spinning (48). 

Classes of Estimation Methods Table 1.1.1 summarizes the property estimation methods considered in this book. Quantitative property-property relationships (QPPRs) are defined as mathematical relationships that relate the query property to one or several properties. QPPRs are derived theoretically using physicochemical principles or empirically using experimental data and statistical techniques. By contrast, quantitative structure-property relationships (QSPRs) relate the molecular structure to numerical values indicating physicochemical properties. Since the molecular structure is an inherently qualitative attribute, structural information has first to be expressed as a numerical values, termed molecular descriptors or indicators before correlations can be evaluated. Molecular descriptors are derived from the compound structure (i.e., the molecular graph), using structural information, fundamental or empirical physicochemical constants and relationships, and stereochemcial principles. The molecular mass is an example of a molecular descriptor. It is derived from the molecular structure and the atomic masses of the atoms contained in the molecule. An important chemical principle involved in property estimation is structural similarity. The fundamental notion is that the property of a compound depends on its structure and that similar chemical stuctures (similarity appropriately defined) behave similarly in similar environments. 

The physical properties ol styrene have been well studied by a number of investigators. No physical property data are avuilublc on cydooctudtenc. Thus, all its properties were estimated from the molecular structure These methods will give the same values whether ihe compound U 1.3. 1.4- 1.5- or 1,6-cyclooct dlene... 

As originally conceived, this handbook was to include worked examples of estimation methods for a group of benchmark chemicals for which reliable properties exist. The advantage of this approach is that the reader is likely to find it easier to apply the estimation methods if there are examples to follow. This proved to be more difficult than was expected and not all these benchmark chemicals are fully treated. Ideally, the estimated values should correspond closely with measured values. In some cases there are significant discrepancies, and this serves to reinforce the message that there remains a need to improve these methods in both accuracy and scope. The subject of estimating chemical properties from molecular structure and from related properties is thus a fruitful topic of research, and will remain so for many years into the future. 

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