A stoichiometric approach to quantitative structure-property relationships (QSPR)

Area of Science:

• Chemistry
• Computational Chemistry
• Chemical Informatics

Background:

• Quantitative Structure-Property Relationships (QSPR) analysis conventionally uses ordinary least-squares (OLS).
• Existing QSPR methods may benefit from integration with fundamental chemical principles for enhanced interpretability.
• Identifying outliers in QSPR models is crucial for data quality and model reliability.

Purpose of the Study:

• To present an unusual analogy between QSPR, stoichiometry, chemical thermodynamics, and kinetics.
• To modify conventional OLS-QSPR analysis by incorporating concepts from chemical formalism.
• To introduce a novel approach for outlier detection in QSPR.

Main Methods:

• Modification of ordinary least-squares (OLS) QSPR analysis to minimize residuals under linear constraints.
• Visualization and definition of linear relations among residuals, termed 'isostructural reactions'.
• Partitioning residuals into contributions from isostructural reactions analogous to response reactions (RERs) from thermodynamics and kinetics.

Main Results:

• Demonstration of a strong analogy between QSPR residuals and chemical stoichiometry formalism.
• Introduction and validation of 'isostructural reactions' as a tool to understand QSPR.
• Successful application of the isostructural RERs approach for effective outlier detection in QSPR analysis.

Conclusions:

• The proposed isostructural RERs approach offers a deeper understanding of QSPR models.
• This method provides a chemically intuitive framework for analyzing QSPR data.
• The isostructural RERs approach is a valuable tool for enhancing the robustness and reliability of QSPR studies.