Analysis of QSAR Models and Their Relationship with New Compound Design Using HKSA

This study focuses on the analysis of Quantitative Structure–Activity Relationship (QSAR) models and their critical role in guiding the design of new compounds through. QSAR is a computational approach that establishes mathematical correlations between molecular descriptors and biological activity, allowing researchers to predict the pharmacological potential of untested compounds. In this research context, various descriptors—including physicochemical, topological, electronic, and quantum chemical parameters—are analyzed to determine their significance in influencing biological responses. The reliability of QSAR models is ensured through rigorous validation processes such as cross-validation, external test sets, and applicability domain assessment, which confirm both accuracy and generalizability. By interpreting descriptor contributions and analyzing model outputs, researchers can identify structural features that enhance or hinder activity, thereby providing a rational basis for molecular modification. Integration with advanced computational tools, including 3D-QSAR, pharmacophore modeling, and machine learning algorithms, further strengthens predictive performance and enables efficient screening of large compound libraries. The analysis highlights how QSAR not only predicts activity but also serves as a blueprint for rational compound optimization, leading to the design of novel molecules with improved potency, selectivity, and safety. Ultimately, this study emphasizes the importance of QSAR analysis as a strategic tool in modern drug discovery, accelerating the development of innovative therapeutic agents.