Quantitative Structure Activity Relationship (QSAR)

Quantitative Structure–Activity Relationship (QSAR), also known as Hubungan Kuantitatif Struktur dan Aktivitas (HKSA), is a pivotal computational strategy in the design of novel compounds. This review provides a comprehensive overview of the QSAR/HKSA framework, encompassing theoretical concepts, descriptor generation, physicochemical property estimation, model construction, validation, and interpretation. By employing theoretical descriptors and calculated physicochemical parameters, QSAR enables the establishment of quantitative correlations between molecular structure and biological activity. Advances in modeling have moved beyond classical linear regression toward sophisticated machine learning and deep learning techniques, offering improved predictive capabilities while demanding rigorous validation to ensure reliability. Validation remains central, with the OECD principles underscoring the importance of well-defined endpoints, algorithm transparency, statistical robustness, and applicability-domain assessment. When appropriately validated, QSAR models function as powerful predictive tools, supporting compound prioritization, guiding structural optimization, and integrating modern biological data for multi-endpoint predictions. Taken together, these developments confirm that QSAR/HKSA not only accelerates the discovery process but also enhances rational decision-making in drug design by bridging theoretical modeling with practical applications.