Integrated In-Silico Docking and QSAR Modeling of Biologically Active Schiff Base Derivatives for Drug Discovery Applications

Schiff base derivatives have emerged as a versatile class of biologically active compounds withsignificant potential in drug discovery due to their structural diversity and strong coordination abilitywith biomolecular targets. The present study focuses on an integrated in-silico approach combiningmolecular docking and quantitative structure–activity relationship (QSAR) modeling to evaluate thetherapeutic potential of Schiff base derivatives. A series of structurally diverse Schiff base ligandswere designed and optimized using computational chemistry tools, followed by molecular dockingagainst selected target proteins associated with key disease pathways. The docking analysis wasperformed to predict binding affinity, interaction patterns, and stability within the active sites of thetarget receptors. Hydrogen bonding, hydrophobic interactions, and π–π stacking were analyzed tounderstand ligand–protein recognition mechanisms. In parallel, QSAR modeling was employed tocorrelate molecular descriptors with observed biological activity, enabling the identification ofstructural features responsible for enhanced pharmacological response. The developed QSAR modeldemonstrated strong predictive reliability and statistical significance, supporting its applicability invirtual screening. The combined docking–QSAR strategy effectively prioritized lead compounds withimproved binding affinity and favorable drug-likeness properties. Overall, this integratedcomputational workflow provides valuable insights into structure-based drug design and acceleratesthe identification of promising Schiff base derivatives for further experimental validation in drugdevelopment pipelines.