Quantitative property prediction

If the consequences of an incident can be predicted quantitatively (property loss and the possible number of fatalities), then a quantitive assessment can be made of the risk. 

If we are aiming for a quantitatively correct prediction of the behavior and properties of a specific polymer, we need to employ an optimized and carefully validated force field for this specific polymer. In the literature one often finds simulation work using force fields that do not fulfill these criteria but where instead the authors use polymer-xy-like models. Although these models fail to reproduce the properties of the polymer they claim to model... 

In heterocyclic chemistry, a quantitative evaluation of the aromatic character is a necessity as new heterocyclic systems are designed and synthesized and need to be evaluated in connection with property predictions. Three major approaches to the quantization of aromaticity exist ... 

The qualitative analysis of retention behaviour in liquid chromatography has now become possible. Quantitative retention-prediction is, however, still difficult the prediction of retention time and the optimization of separation conditions based on physicochemical properties have not yet been completely successful. One reason is the lack of an ideal stationary phase material. The stationary phase material has to be stable as part of an instrument, and this is very difficult to achieve in normal-phase liquid chromatography because the moisture in organic solvents ages the silica gel. 

Single property prediction through QSAR modeling (Quantitative Structure-Activity Relationship). 

These qualitative explanations, whether they be hard-soft or ionic-covalent or Class A-Class B, all suffer from the arbitrary way in which they can be employed. All Lewis acid-base type interactions are composed of some electrostatic and some covalent properties, i.e., hardness and softness are not mutually exclusive properties. Predictions are straightforward when dealing with the extremes, but with other more ambiguous systems, one can be very arbitrary in explaining results and the predictive value is impaired. What is needed is a quantitative assessment of the essential factors which can contribute to donor strength and acceptor strength. Proper combination of these parameters should produce the enthalpy of adduct formation. Until this can be accomplished, one could even question the often made assumption that the strength of the donor-acceptor interaction is a function of the individual properties of a donor or acceptor. 

A QSAR seeks to relate quantitative properties (descriptors) of a compound with other properties such as drug-like activity or toxicity. The essential assumption of QSAR is that quantities that can be conveniently measured or calculated for a compound can be used to accurately predict another property of interest (e.g., antibacterial activity) in a nontrivial way. QSAR has become an integral part of screening programs in pharmaceutical drug-discovery pipelines of small compounds and more recently in toxicological studies (69). However, the use of QSAR modeling applied to the search for antimicrobial peptides is relatively recent. Advances in this area are reviewed in brief here. 

A practically useful predictive method must provide quantitative process prediction from accessible physical property data. Such a method should be physically realistic and require a minimum number of assumptions. A method which is firmly based on the physics of the separation is likely to have the widest applicability. It is also an advantage if such a method does not involve mathematics which is tedious, complicated or difficult to follow. For the pressure driven processes of microfiltration, ultrafiltration and nanofiltration, such methods must be based on the microhydrodynamics and interfacial events occurring at the membrane surface and inside the membrane. This immediately points to the requirement for understanding the colloid science of such processes. Any such method must account properly for the electrostatic, dispersion, hydration and entropic interactions occurring between the solutes being separated and between such solutes and the membrane. 

Understanding the relationship between the composition of a mixture and its properties is fundamental to the development of formulated products. In the pesticide industry, formulation chemists seek to translate such an understanding into products that meet criteria established for properties such as suspensibility, emulsibility, storage stability, compatibility, and most importantly, biological activity. The preferable way to acquire the necessary knowledge is to deduce the properties of mixtures in terms of mechanisms that are operative at the microscopic level. However, mixtures are extremely complex systems and the available theory is usually insufficient for developing useful theoretical models. For example, we are unable quantitatively to predict, on the basis of molecular theory, the suspensibility of a wettable powder from a knowledge of its composition. 

Kubinyi, H. (1988a). Current Problems in Quantitative Structure-Activity Relationships. In Physical Property Prediction in Organic Chemistry (Jochum, C, Hicks, M.G. and Sunkel, J., eds.). Springer-Verlag, Berlin (Germany), pp. 235-247. 

Brauner, N., Stateva, R.P., Cholakov, G.S. and Shacham, M. (2006) Structurally targeted quantitative structure-property relationship method for property prediction. Ind. Eng. Chem. Res., 45, 8430-8437. 

Kubinyi H. Current problems in quantitative structure-activity relationships. In Jochum C, Hicks MG, Sunkel J, eds. Physical Property Prediction in Organic Chemistry. Berlin Springer-Verlag, 1988 235-247. 

H. Kubinyi, Phys. Property Predict. Org. Chem., Proc. Beilstein Workshop, Schinss Knrb, Italy, May 16-20, 1988, p. 2335. Current Problems in Quantitative Structure-Activity Relationships. 

Giles, M.R. 1997. Diagenesis A Quantitative Perspective. Implications for Basin Modelling and Rock Property Prediction. Kluwer Academic Publisher. 526 p. 

Hence, the results stated above demonstrated that the cluster model of polymers amorphous state stmcture and fractal analysis allowed quantitative prediction of mechanical properties for pol5miers film samples, prepared from different solvents. Let us note, that the properties prediction over the entire length of the diagram a- was performed within the framework of one approach and with precision, sufficient for practical applications. This approach is based on strict physical substantiation of the analytical intercommunication between structures of a macromolecular coil in solution and pol5miers condensed state [201]. 

Even though HMO theory cannot be relied upon for quantitatively correct predictions for some physical properties, it nevertheless provides a convenient framework for the development of a number of useful concepts in molecular bonding and reactivity. Among these are k electron density, charge density, bond order, and free valence. We calculate the electron density (p,) at each atom by summing the electron density at that atom for each occupied molecular orbital. We have defined ipi in equation 4.1 ... 

Zakharov AV, Peach ML, Sitzmann M et al (2012) Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes. Future Med Chem 4 1933-1944. doi 10.4155/fmc.l2.152 Zakharov AV, Lagunin AA, Filimonov DA, Poroikov VV (2012) Quantitative prediction of antitarget interaction profiles for chemical compounds. Chem Res Toxicol 25 2378-2385. doi 10.1021/tx300247r... 

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