Materials modeling QSPR/QSAR

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,... 

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

The quantitative structure-activity and structure-property relationships (QSARs/ QSPRs) have become efficient tools to study complex chemical and biochemical systems. In principle, once a correlation between the molecular structure and activity/ property is found, any number of compounds, including those not yet synthesized, can be readily screened on the computer in order to predict the structures with the desired properties. Subsequently, the respective chemical compounds can be synthesized and tested in the laboratory. Thus the QSAR/QSPR approach conserves resources and accelerates the process of development of new drugs, materials, etc. In addition to the structure predictions, the QSAR/QSPR models often help to understand the physical nature of the processes and interactions behind the property or activity studied. 

K. Wu, B. Natarajan, L. Morkowchuk, M. Krein, C.M. Breneman, From dmg discovery QSAR to predictive materials QSPR the evolution of descriptors, methods, and models, pp. 385 10. in Informatics dor materials science and engineering, ed. by K. Rajan (Elsevier, Amsterdam, 2013)... 

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