Notes on quantitative structure-property relationships (QSPR), part 3: density functions origin shift as a source of...

Area of Science:

• Computational Chemistry
• Quantum Chemistry
• Cheminformatics

Background:

• Classical quantitative structure-property relationships (QSPR) often face the dimensionality paradox and rely heavily on statistical methods.
• Existing QSPR approaches can be computationally intensive and limited in scope.
• Quantum similarity frameworks provide a basis for developing novel predictive models.

Purpose of the Study:

• To introduce a general algorithm for a variant of quantum quantitative structure-property relationships (QQSPR).
• To enable the estimation of unknown molecular properties using known property data and quantum mechanical calculations.
• To overcome limitations of classical QSPR, including the dimensionality paradox.

Main Methods:

• The QQSPR procedure utilizes geometrical origin shifts over molecular density function sets.
• It employs quantum mechanical expectation values to establish causal relationships.
• The approach allows for flexibility, using geometrical assessment, simple statistics, or both.

Main Results:

• The described QQSPR algorithm successfully predicts molecular properties with excellent accuracy.
• It overcomes the dimensionality paradox inherent in classical descriptor-based QSPR.
• A Fortran 95 program, QQSPR-n, is available, facilitating practical applications.

Conclusions:

• The developed QQSPR method offers a robust and efficient alternative to classical QSPR.
• The approach provides a computationally accessible route for property prediction in various chemical contexts.
• An equivalent classical QSPR formalism in molecular space can also be developed from this framework.