Quantitative structure-activity relationship molecular modeling

W. Guba, G. Cruciani, Molecular Modeling and Prediction of Bioactivity, Proceedings of the European Symposium on Quantitative Structure-Activity Relationships Molecular Modeling and Prediction of Bioactivity, 12th, Copenhagen, Denmark, Aug. 23-28,1998. 

The validity of a model is always limited to a certain domain in the parameter space. For example, if a quantitative structure-activity relationships (QSAR) model is specified for nonpolar organic chemicals in the log range from 2 to 6 and has a molecular weight below 700, then an application to substances outside this range is an improper extrapolation. Note that the parameter space may be difficult to discern for example, combinations of low values for one variable and high values for another could constitute an extrapolation if such combinations had been missing in the validation or specification of the model. Exceedence of model boundaries introduces additional uncertainty at best, but can also lead to completely incorrect outcomes. 

Quantitative structure-activity relationship (QSAR) models for kinetic rate constants and molecular descriptors, such as dipole moment, EHOMO, ELUMO/... 

Clearly, molecular structure influences the reaction kinetics of organic compounds during their photocatalytic oxidation. This relationship between degradability and molecular structure may be described using quantitative structure-activity relationship (QSAR) models. QSAR models can be developed to predict kinetic rate constants for organic compounds with similar chemical structures. The following section discusses QSAR models developed by Tang and Hendrix (1998) as well as those developed by other researchers. 

The semiempirical methods are dramatically (100- to 1,000-fold) faster and are suitable tor many applications. The results of AMI calculations often are used as the starting points tor parameterizations (e.g., atomic charges) of force fields in molecular dynamics simulations (27,28) and CoMFA quantitative structure-activity relationship (QSAR) modeling studies (29). 

This chapter is based on the authors 12 years of combined experience regarding quantitative structure-activity relationship (QSAR) modeling. The intent is to present a discussion of principles and caveats aimed at the occasional end user, while offering some in-depth comments for those experienced in the area of three-dimensional (3D) QSAR. More than 200 CoMFA papers have been published since the initial inclusion of comparative molecular field analysis in SYBYL in 1988. It would have been beyond the scope of this chapter to critique all these reports. Instead, we focus on providing a working knowledge on the generation, critical evaluation, and meta-analysis of 3D-QSAR models. 

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. 

Thiadiazole 1 and its derivatives were used as model compounds for the calculation of molecular parameters related to physical properties for their use in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies <1999EJM41, 2003IJB2583, 2005JMT27>. 

Basak, S. C., Mills, D. Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors. ARKIVOC 2005, 2005(x), 308-320. 

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