Pemodelan dan Analisis Hubungan kuantitatif struktur dan aktivitas (QSAR/HKSA) dan Desain Senyawa Baru

Quantitative Structure–Activity Relationship (QSAR) modeling, or Hubungan Kuantitatif Struktur–Aktivitas (HKSA), is a computational method that establishes mathematical relationships between molecular descriptors and biological activity, enabling predictive insights that accelerate compound design and drug discovery. By utilizing physicochemical, electronic, and steric parameters, QSAR models can predict efficacy, selectivity, and safety of compounds before synthesis, reducing experimental costs and time. The integration of advanced machine learning and deep learning methods, such as Random Forests, support vector machines, and chemical language models, has improved predictive accuracy and expanded the applicability of QSAR across diverse datasets. Furthermore, the incorporation of theoretical descriptors derived from quantum mechanics, including orbital energies and electron densities, enhances interpretability and supports ADMET prediction. Validation remains a cornerstone of reliable QSAR modeling, requiring cross-validation, external test sets, and applicability domain analysis to ensure robustness and prevent overfitting. Importantly, QSAR/HKSA not only predicts activity but also guides rational design by identifying structural modifications that optimize potency and minimize toxicity. Applications extend from drug discovery to regulatory and environmental sciences, where predictive toxicology informs chemical risk assessment. Collectively, QSAR/HKSA provides a cost-effective, mechanistic, and efficient framework for modern compound design and innovation in computational chemistry.