CURRENT PERSPECTIVES ON QSAR AND ANALYTICAL METHODS IN MEDICINAL CHEMISTRY

Quantitative structure-activity relationship (QSAR) modelling and its integration with pharmaceutical analytical methods
represent a synergistic scientific framework that quantitatively links molecular structure to biological activity, physicochemical
properties, and ADMET endpoints, enabling predictive compound optimisation across the medicinal chemistry pipeline [1, 2].
QSAR was established by Hansch and Fujita (1964) through linear free energy relationship analysis of congeneric compound
series, and has evolved through Free-Wilson additivity analysis, 3D QSAR (CoMFA; CoMSIA), 2D fingerprint-based ML
QSAR, graph neural network (GNN) QSAR, and AI transformer QSAR trained on ChEMBL's 20+ million bioactivity data
points [3, 4]. Contemporary QSAR encompasses: 2D QSAR using molecular fingerprints (ECFP4/6) and physicochemical
descriptors with machine learning (random forests; gradient boosting; GNN); 3D QSAR using comparative molecular field
analysis (CoMFA) and similarity indices (CoMSIA) in pharmacophore-aligned 3D space; AI/ML QSAR using graph neural
networks and transformer architectures achieving state-of-the-art ADMET prediction; and regulatory QSAR under ICH M7 for
genotoxic impurity mutagenicity assessment as an alternative to experimental Ames test [5, 6]. QSAR-analytical integration is
bidirectional: experimental analytical data (logD; pKa; microsomal CLint; Caco-2 Papp; hERG IC50) trains QSAR models;
QSAR predictions guide analytical method prioritisation and synthesis decisions; and regulatory QSAR acceptance validates
computational methods as pharmaceutical analytical tools [7, 8]. QSRR (Quantitative Structure-Retention Relationships) applies
QSAR methodology to predict HPLC chromatographic retention, enabling in-silico analytical method development and impurity
elution order prediction [9, 10]. This review consolidates current QSAR methodologies, analytical data integration, regulatory
applications, and the emerging AI-QSAR frontier.