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

ABSTRACT Quantitative Structure–Activity Relationship (QSAR) modeling is a computational approach that establishes quantitative correlations between chemical structure and biological activity through mathematical models. Based on the premise that small structural modifications lead to measurable changes in activity, QSAR leverages molecular descriptors—such as physicochemical properties, steric indices, and electronic parameters—to represent chemical entities numerically. These descriptors are integrated into statistical or machine learning frameworks to generate predictive models capable of estimating activities of untested compounds. Commonly employed modeling techniques include multiple linear regression, partial least squares, random forest, and artificial neural networks. Rigorous validation using internal (cross-validation, Q²) and external validation sets (R², RMSE) is essential to ensure model robustness and avoid overfitting. QSAR models facilitate the rational design of new compounds by identifying structural features that enhance target affinity, reduce toxicity, or improve pharmacokinetic profiles. This data-driven strategy accelerates lead optimization in drug discovery, supports safer agrochemical design, and aids in predictive toxicology while reducing experimental burden. Recent advances integrating deep learning and ensemble methods have improved predictive power, but also require careful interpretability and applicability-domain assessment to maintain scientific reliability. Overall, QSAR serves as a strategic bridge between theoretical chemistry, computational modelling, and molecular biology, enabling efficient screening and design of novel bioactive molecules and contributing to more sustainable and cost-effective development pipelines