The quantitative structure-property relationship (QSPR) is used in the field of chemistry, and relates bio-physico-chemical properties of chemical compounds to their structures. In biological contexts, these are also called quantitative structure-activity relationships (QSAR).

Rationale

The basic rationale behind the use of QSPR is the fact that there are strong trends. It is well known for instance that within a particular family of chemical compounds, especially of organic chemistry, that there are strong correlations between structure and observed properties. One example is the relationship between the number of Carbons in Alkanes and their boiling points. There is a clear trend in the increase of boiling point with an increase in the number carbons and this serves as a means for predicting the boiling points of higher alkanes.

Method

In many cases the approaches use curve fitting, interpolation and extrapolation techniques. The predictor variables can be a variety of chemical descriptors:

• 1D descriptors - molar weight , number and composition of atoms
• 2D descriptors - bonds
• topological descriptors - numerical indices based on the topology of the atoms and their bonds (chemical conformation, quartenary structure )
• electronic descriptors - these descriptors characterize the eletronic environment of a molecule. Examples include LUMO and HOMO energies, and electronegativity.
• hybrid descriptors - this class of descriptors are essentially combinations of the other types. One example is the charged partial surface area descriptor that combines partial charges and molecule surface areas. Other examples include hydrogen bonding descriptors and hydrophobic surface area descriptors.
• other kinds of descriptors

It is necessary that the predicted variable has been compared with a number of observations made to sufficient accuracy for a variety of pure chemicals belong to the same family .

See also

cheminformatics, computational chemistry, data clustering, regression analysis, Craig plot , discriminant analysis , ANN, ab initio prediction , Principal components analysis