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

Abstract

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.