A Comprehensive Examination of Quantitative Structure Activity Relationship Utilizing by Hansch Parameters and Hammett Constants

Author

Mr. P. Nanthagopal, Dr. D. Nagavalli, Ms S. Priyadharshini, R.Ilakkiya Shree, G.V.Kamlaishri

Abstract

 Quantitative structure–activity relationship (QSAR) analysis provides a mechanistic and statistically interpretable framework for translating molecular structure into biological activity and guiding rational drug discovery. Classical Hansch and Hammett approaches established the foundations of medicinal chemistry by quantitatively linking hydrophobic, electronic, and steric substituent effects with pharmacological response. This review critically examines the theoretical basis, historical evolution, methodological workflow, and contemporary relevance of Hansch–Hammett QSAR, with particular emphasis on hydrophobic substituent constants, Hammett σ parameters, steric descriptors, biological activity transformations, and multiple linear regression modelling. Key determinants of model reliability—including congeneric compound selection, descriptor orthogonality, data quality, multicollinearity, nonlinear structure–activity relationships, outlier influence, statistical significance, and internal and external validation—are systematically discussed. Particular attention is given to applicability-domain assessment and the distinction between statistical fit and genuine predictive performance. The complementary nature of Hansch and Hammett analyses is further considered in relation to modern molecular descriptors, computational chemistry, and machine-learning strategies. Despite limitations arising from restricted datasets, descriptor interdependence, biological variability, and simplified physicochemical representations, classical QSAR retains substantial value because of its transparency, computational efficiency, and chemically interpretable parameters. Integrating these established principles with contemporary data-driven methodologies can enhance mechanistic understanding, descriptor-guided optimization, and predictive modelling, reinforcing classical QSAR as a durable foundation for modern computational medicinal chemistry.

Keywords

QSAR; Hansch Analysis; Hammett Constants; Hydrophobicity; Electronic and Steric Effects; Linear Free-Energy Relationships; Molecular Descriptors; Drug Design and Lead Optimization.

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