Integrated In-Silico Docking and QSAR Modeling of Biologically Active Schiff Base Derivatives for Drug Discovery Applications - Rashmi Ohlan; Dr. Preeti Chahal (The Bioscan, 2026)
Schiff base derivatives have emerged as a versatile class of biologically active compounds with significant potential in drug discovery due to their structural diversity and strong coordination ability with biomolecular targets. The present study focuses on an integrated in-silico approach combining molecular docking and quantitative structure–activity relationship (QSAR) modeling to evaluate the therapeutic potential of Schiff base derivatives. A series of structurally diverse Schiff base ligands were designed and optimized using computational chemistry tools, followed by molecular docking against selected target proteins associated with key disease pathways. The docking analysis was performed to predict binding affinity, interaction patterns, and stability within the active sites of the target receptors. Hydrogen bonding, hydrophobic interactions, and π–π stacking were analyzed to understand ligand–protein recognition mechanisms. In parallel, QSAR modeling was employed to correlate molecular descriptors with observed biological activity, enabling the identification of structural features responsible for enhanced pharmacological response. The developed QSAR model demonstrated strong predictive reliability and statistical significance, supporting its applicability in virtual screening. The combined docking–QSAR strategy effectively prioritized lead compounds with improved binding affinity and favorable drug-likeness properties. Overall, this integrated computational workflow provides valuable insights into structure-based drug design and accelerates the identification of promising Schiff base derivatives for further experimental validation in drug development pipelines.