Quantitative Structure and Activity Relationship (QSAR) Modeling and Analysis and Design of New Compounds

Quantitative Structure–Activity Relationship (QSAR), also referred to as Hubungan Kuantitatif Struktur dan Aktivitas (HKSA), represents a cornerstone methodology in computational chemistry and drug discovery, enabling the correlation of molecular structures with biological activities to support rational compound design. This study highlights the comprehensive process of QSAR/HKSA modeling, beginning with dataset preparation from open-access chemical databases, calculation of physicochemical descriptors such as lipophilicity, polarity, and electronic properties, and subsequent model development using statistical and machine learning approaches. Descriptor-based analyses were integrated with advanced computational tools to identify structural features that strongly influence biological activity. Model validation was conducted using both internal and external strategies to ensure robustness, reproducibility, and predictive accuracy. Furthermore, the interpretation of QSAR models was emphasized as a critical step in translating computational outcomes into actionable guidelines for structural modification and lead optimization. The integration of QSAR with complementary in silico techniques, including molecular docking and molecular dynamics, was also addressed, highlighting its role in refining compound–target interactions. The results underscore the potential of QSAR/HKSA not only to accelerate the discovery of novel drug candidates but also to minimize experimental costs and enhance safety by providing rational, data-driven insights. Overall, the study demonstrates that QSAR/HKSA serves as both a predictive and interpretative framework for the design of new compounds across pharmaceutical and environmental applications.